Analysis of B-cell markers in MCS device patients

Statistical Workbook

Introduction

Raw data

The raw data is shown below. There were 214 rows and 80 columns, consisting of 32 patients sampled over 7 timepoints. However, there is a significant amount of missing data, resulting in only 166 usable datapoints.

missing.ix <- Reduce(intersect, apply(df[,bcellcyto], 2, function(x) which(is.na(x))))
df.raw <- df[-missing.ix,]
df.raw <- df.raw[order(df.raw$PatientID),]
rownames(df.raw) <- 1:nrow(df.raw)
kable(df.raw, 
      digits = 3,
      row.names = T,
      caption = "Raw Data"
) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%", height = "300px") 

Raw Data

	PatientID	Time	Age	AgeGreater60	Sex	LowIntermacs	InterMACS	RVAD	Sensitized	VAD Indication	Device Type	Outcome	Survival	num Total PBMC	num lymph	lymph	live lymph	CD3 of live lymph	CD19 of live lymph	CD19+CD27-	CD19+CD27+	CD27+38++plasma blasts	CD27-38++ transitional	CD27-IgD+ mature naive	CD27+IgD- switched memory	CD27-IgD- switched memory	CD27+IgD+ unswitched memory	CD27+IgD-IgM+ switched memory	CD27+IgD+IgM+ nonswitched memory	CD19+27+IgG+IgM- memory	CD19+24dim38dim naive mature	CD19+24+38++transitional	CD19CD24hiCD38-memory	CD19+27-38+CD5+transitionals	CD19+CD268+	CD268 of +27-38++transitional	CD19+CD11b+	CD19+CD5+	CD19+CD27+CD24hi	CD19+CD5+CD24hi	CD19+CD5+CD11b+	CD19+27+IgD-38++IgG ASC	IL-12(p40)	IL-12(p70)	IFN-g	TNF-a	TNF-b	IL-4	IL-5	IL-9	IL-10	IL-13	IL-17A	IL-1a	IL-1b	IL-2	IL-3	IL-6	IL-15	TGF-a	IFN-a2	IL-8	GRO	Eotaxin	MDC	IP-10	MCP-1	MCP-3	Fractalkine	MIP-1a	MIP-1b	GM-CSF	IL-7	G-CSF	VEGF	EGF	FGF-2	Flt-3L	IL-1RA	sCD40L
1	1	0	65	older	Male	High	2	No	NA	BTT	HMII	Alive s/p OHT	alive	169154	35496	20.98	99.53	25.37	19.82	86.02	13.98	1.26	2.54	57.81	11.74	28.06	2.39	21.74	14.36	0.72	81.05	2.86	13.60	0.58	95.22	84.83	10.47	4.33	8.50	1.39	3.13	1.09	1.71	2.16	124.000	20.926	2.72	2.500	1.010	7.273	4.617	1.760	21.20	277.000	3.483	9.878	1.160	12.334	1.090	5.71	2.18	45.38	126.00	119.000	525.00	1174	392	3.03	27.25	2.030	47.654	60.843	1.268	33.267	486.000	54.27	6.05	1.92	4.47	793.00
2	1	1	65	older	Male	High	2	No	NA	BTT	HMII	Alive s/p OHT	alive	63082	9915	15.72	99.26	32.45	26.26	90.06	9.94	0.89	2.09	54.27	7.70	35.71	2.32	31.10	20.47	1.57	84.06	2.59	10.79	0.26	97.02	92.59	7.50	3.13	6.42	1.32	2.44	1.49	1.71	1.71	156.000	24.857	2.72	2.500	1.010	1.270	48.726	1.760	29.97	388.000	1.350	1.952	1.160	109.000	5.715	3.89	3.18	102.00	191.00	95.541	463.00	1079	552	3.03	35.53	2.030	62.089	12.293	2.005	109.000	509.000	64.48	26.13	1.92	19.50	841.00
3	1	3	65	older	Male	High	2	No	NA	BTT	HMII	Alive s/p OHT	alive	75921	21721	28.61	99.31	24.86	33.01	86.38	13.62	1.54	1.26	45.74	10.98	40.44	2.84	31.80	18.04	1.15	86.21	4.82	6.56	0.24	90.41	73.33	10.00	7.25	10.12	3.22	5.67	2.05	32.32	14.40	259.000	28.330	14.74	12.770	3.369	5.615	28.055	7.406	54.61	438.000	3.867	8.638	3.690	57.616	12.778	5.85	56.84	121.00	193.00	186.000	510.00	1365	464	35.08	152.00	2.030	87.775	44.032	10.100	81.038	687.000	70.06	117.00	1.92	151.00	801.00
4	1	5	65	older	Male	High	2	No	NA	BTT	HMII	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.71	5.07	134.000	41.230	2.72	2.813	1.809	4.930	23.012	1.760	26.38	316.000	1.219	1.230	2.735	15.579	5.021	2.73	25.80	78.31	231.00	213.000	462.00	2053	583	4.14	104.00	2.030	55.375	49.222	6.820	29.004	414.000	61.03	43.25	1.92	64.51	1230.00
5	1	8	65	older	Male	High	2	No	NA	BTT	HMII	Alive s/p OHT	alive	213808	36002	16.84	99.19	35.95	14.55	67.94	32.06	4.06	2.17	27.92	28.92	39.64	3.52	20.31	9.10	1.33	68.69	7.10	20.42	1.88	87.38	44.25	10.78	11.12	19.15	5.54	7.35	1.07	1.71	3.83	228.000	23.577	2.81	2.500	1.666	6.792	39.103	1.760	43.65	450.000	2.412	6.904	2.197	19.163	2.445	3.75	19.72	56.79	134.00	184.000	496.00	1214	430	3.03	90.07	2.030	72.549	44.681	4.448	51.520	735.000	78.74	94.11	1.92	133.00	912.00
6	1	21	65	older	Male	High	2	No	NA	BTT	HMII	Alive s/p OHT	alive	106970	31867	29.79	99.18	42.36	15.02	84.69	15.31	1.94	2.11	55.41	13.14	29.13	2.32	20.22	12.88	2.08	83.21	5.20	9.50	1.10	94.44	73.00	9.04	6.99	9.22	2.80	4.78	1.44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
7	2	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	47.38	62.54	800.000	44.293	47.38	58.846	142.000	8.004	27.135	23.250	152.00	1482.000	15.724	10.414	10.464	169.000	15.490	9.98	136.00	144.00	414.00	217.000	834.00	2701	464	74.28	328.00	44.782	220.000	47.925	53.810	185.000	1845.000	220.00	275.00	661.00	251.00	1300.00
8	2	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.71	108.00	305.000	26.686	2.72	2.500	24.287	1.270	40.482	1.760	92.32	396.000	0.858	1.230	1.228	177.000	13.937	8.89	16.74	122.00	86.72	106.000	470.00	1039	267	10.10	84.06	17.074	190.000	11.123	3.608	17.674	1146.000	145.00	311.00	365.00	583.00	1584.00
9	2	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	4.78	65.82	441.000	41.771	8.78	13.363	43.505	3.412	20.281	4.002	99.92	620.000	2.755	2.491	5.542	132.000	17.441	9.57	66.07	124.00	334.00	227.000	441.00	681	419	24.50	182.00	19.737	214.000	19.603	12.708	78.434	1314.000	146.00	311.00	439.00	251.00	1349.00
10	2	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	16.71	59.25	490.000	44.652	16.00	36.320	46.100	5.500	18.023	9.218	109.00	700.000	4.262	3.356	7.955	133.000	16.270	9.71	90.20	136.00	386.00	245.000	434.00	899	587	27.78	205.00	22.552	205.000	26.568	16.970	101.000	1464.000	153.00	361.00	469.00	205.00	1335.00
11	2	8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.71	114.00	563.000	71.393	2.81	25.856	61.646	2.705	6.884	1.760	143.00	749.000	2.086	4.590	2.553	148.000	9.354	10.52	41.30	140.00	249.00	258.000	479.00	1372	764	26.17	223.00	34.562	258.000	25.928	6.167	59.758	1701.000	159.00	489.00	525.00	263.00	801.00
12	3	0	81	older	Male	Low	3	No	NA	DT	HMII	Died	dead	119377	77509	64.93	99.61	58.50	7.99	62.67	37.33	9.10	4.82	36.73	31.90	25.62	5.74	11.47	8.95	0.40	69.54	7.69	9.41	2.61	76.44	57.91	23.29	20.76	15.39	5.42	14.90	2.60	6.39	6.65	11.372	21.497	3.51	2.320	6.303	1.590	6.039	2.111	5.18	4.727	1.920	1.450	2.558	38.074	5.729	2.57	19.65	32.02	123.00	133.000	380.00	1899	1054	26.14	112.00	2.230	32.026	20.483	7.783	58.902	70.513	7.02	95.30	0.39	89.23	624.00
13	3	1	81	older	Male	Low	3	No	NA	DT	HMII	Died	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	41.70	5.35	39.418	40.838	13.66	17.416	2.457	4.050	57.842	9.006	7.14	46.122	4.879	6.666	3.299	119.000	26.234	2.86	67.14	112.00	206.00	180.000	411.00	1271	4441	35.46	169.00	33.419	100.000	54.262	5.253	43.646	118.000	24.49	79.63	0.39	224.00	1714.00
14	3	3	81	older	Male	Low	3	No	NA	DT	HMII	Died	dead	96940	57658	59.48	99.70	55.52	9.21	72.15	27.85	3.85	3.82	48.41	21.75	23.49	6.35	12.10	16.04	0.28	75.30	3.72	6.65	2.11	80.52	67.33	15.80	15.23	6.71	1.32	8.86	0.26	17.15	6.00	20.672	15.342	7.00	5.509	7.681	2.040	8.932	3.799	4.45	17.129	2.699	3.725	3.108	25.794	15.326	2.57	74.20	34.62	85.04	162.000	348.00	897	1361	21.65	102.00	6.821	27.057	24.672	5.253	34.257	70.513	13.56	63.96	0.39	118.00	1014.00
15	3	5	81	older	Male	Low	3	No	NA	DT	HMII	Died	dead	154373	73969	47.92	99.65	45.18	8.77	67.36	32.64	6.59	2.77	39.10	26.28	27.95	6.67	17.89	15.71	0.39	71.57	6.26	9.88	1.78	81.86	72.07	20.12	20.17	14.80	5.99	14.65	3.71	4.28	2.29	12.657	16.490	2.31	2.320	6.816	1.590	6.845	1.520	4.74	2.120	1.920	1.450	1.980	99.474	11.334	1.58	47.29	61.82	48.64	192.000	320.00	738	1071	10.05	46.41	2.230	21.777	11.020	1.520	37.403	27.750	2.89	23.91	0.39	52.69	361.00
16	3	8	81	older	Male	Low	3	No	NA	DT	HMII	Died	dead	155610	63375	40.73	99.51	38.42	12.40	71.69	28.31	4.36	2.62	37.82	23.43	33.50	5.24	17.37	14.92	0.60	65.04	2.80	11.23	2.54	83.55	37.56	15.55	15.45	9.55	2.66	10.91	0.28	8.54	4.12	17.955	15.342	3.51	2.320	6.816	1.590	6.439	1.605	5.62	13.939	2.314	1.782	2.042	79.869	8.070	2.42	84.63	34.26	38.88	162.000	335.00	822	1054	16.46	84.38	2.980	19.937	20.483	2.734	21.566	90.169	10.50	41.25	0.39	77.29	562.00
17	3	14	81	older	Male	Low	3	No	NA	DT	HMII	Died	dead	244245	115109	47.13	99.47	47.40	9.37	67.50	32.50	3.99	2.08	50.08	26.01	17.22	6.68	15.34	15.25	0.23	77.86	3.21	10.49	1.48	82.13	59.19	21.45	19.77	14.45	2.57	14.94	0.94	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
18	4	1	58	younger	Male	Low	3	Yes	No	BTT	HMII	Alive s/p OHT	alive	246811	86529	35.06	93.49	67.96	17.48	81.66	18.34	2.05	6.85	51.33	13.69	29.91	5.08	15.68	5.94	0.19	61.95	0.76	13.51	4.07	92.62	34.06	6.90	12.07	8.00	2.54	3.85	0.26	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
19	4	3	58	younger	Male	Low	3	Yes	No	BTT	HMII	Alive s/p OHT	alive	406771	90343	22.21	97.34	49.28	17.02	75.04	24.96	4.47	18.36	28.45	21.86	46.07	3.62	24.97	4.33	0.13	47.41	1.64	19.70	18.15	72.76	8.08	25.69	28.35	13.95	4.66	19.20	0.41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
20	4	21	58	younger	Male	Low	3	Yes	No	BTT	HMII	Alive s/p OHT	alive	330191	81636	24.72	94.57	45.14	14.70	70.51	29.49	8.94	16.23	38.92	23.53	30.83	6.72	23.93	7.07	0.18	46.63	2.35	16.12	8.59	74.45	39.90	24.19	21.18	12.62	3.26	12.93	0.38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
21	5	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	41.24	109.00	187.000	25.955	53.99	42.552	10.747	12.567	51.239	17.031	68.25	64.203	7.364	13.383	10.807	70.212	17.104	10.88	161.00	95.72	536.00	281.000	2294.00	624	929	24.90	228.00	22.644	85.566	58.104	17.699	193.000	1151.000	107.00	183.00	41.52	377.00	6509.00
22	5	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	25.64	18.30	67.494	20.261	8.29	11.301	2.861	4.489	59.406	3.964	20.30	20.414	1.860	4.133	3.291	211.000	13.360	3.81	94.97	78.63	197.00	81.039	880.00	512	1398	16.57	80.75	13.454	68.452	36.398	4.775	71.797	376.000	23.81	173.00	19.21	128.00	2712.00
23	5	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	65.91	44.73	94.077	18.237	66.52	41.525	10.572	11.269	39.553	17.428	29.48	72.616	8.804	10.630	17.422	203.000	21.250	8.49	159.00	54.16	674.00	230.000	562.00	201	607	37.07	181.00	13.404	67.666	56.614	14.252	152.000	646.000	78.65	138.00	26.01	319.00	10443.75
24	5	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	45.56	41.78	173.000	17.642	45.32	43.807	7.681	8.007	29.730	15.356	50.08	131.000	8.403	10.203	12.135	96.449	20.663	8.39	173.00	74.75	989.00	349.000	615.00	223	507	35.46	195.00	16.773	97.826	50.067	27.582	147.000	859.000	103.00	301.00	12.72	199.00	9557.42
25	5	8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	46.23	76.19	129.000	19.211	64.75	40.499	12.238	12.567	41.854	15.844	57.83	87.429	9.848	14.449	16.809	92.658	17.104	9.86	177.00	68.47	747.00	184.000	709.00	491	642	34.12	181.00	16.173	77.694	62.554	17.001	163.000	790.000	101.00	212.00	30.50	325.00	10443.75
26	6	0	67	older	Male	Low	3	No	Yes	BTT	HVAD	Alive s/p OHT	alive	309890	41896	13.52	97.03	47.13	8.08	77.31	22.69	2.74	3.29	59.03	19.80	19.37	1.80	21.10	4.46	19.79	71.98	2.41	17.39	0.20	95.80	75.00	5.79	1.89	16.45	0.91	0.67	0.77	4.28	4.73	52.585	19.181	5.78	2.320	1.758	1.590	16.952	1.605	8.23	2.120	1.920	1.450	2.382	38.750	1.342	12.51	27.01	37.91	534.00	147.000	353.00	425	293	21.65	77.95	6.139	44.075	11.020	5.888	46.738	153.000	45.46	128.00	0.39	129.00	2818.00
27	6	1	67	older	Male	Low	3	No	Yes	BTT	HVAD	Alive s/p OHT	alive	274228	15653	5.71	97.85	35.76	28.87	51.45	48.55	2.60	2.13	30.85	43.13	22.77	3.26	20.09	3.92	30.51	57.58	1.31	28.20	0.18	91.23	59.57	12.08	2.04	34.44	0.38	0.16	0.16	2.18	4.12	60.794	37.589	2.31	2.320	1.125	1.590	80.735	1.520	12.24	2.120	1.920	1.450	1.980	173.000	3.201	15.43	16.01	121.00	231.00	210.000	265.00	359	515	3.98	77.95	48.595	128.000	6.036	3.987	52.863	60.307	7.02	55.00	0.39	65.11	1125.00
28	6	3	67	older	Male	Low	3	No	Yes	BTT	HVAD	Alive s/p OHT	alive	591939	57589	9.73	96.76	38.20	42.54	75.71	24.29	2.14	1.33	53.03	21.26	24.16	1.55	25.50	4.02	20.27	67.18	1.14	21.71	0.11	95.28	66.98	3.79	2.43	17.70	0.65	0.84	0.18	2.18	5.35	60.114	36.632	2.49	2.320	1.496	1.590	48.963	1.520	11.92	2.120	1.920	1.450	2.210	171.000	3.894	13.78	38.10	123.00	342.00	282.000	313.00	467	727	10.05	96.17	8.166	75.587	16.356	5.888	122.000	99.662	15.43	66.76	0.39	124.00	1448.00
29	6	5	67	older	Male	Low	3	No	Yes	BTT	HVAD	Alive s/p OHT	alive	189634	46711	24.63	97.74	34.84	37.32	75.30	24.70	0.70	0.55	46.97	22.05	30.28	0.70	23.67	1.56	31.44	73.33	0.75	18.59	0.00	84.61	86.17	3.44	2.11	18.68	0.94	0.63	0.69	2.22	8.01	26.182	21.497	5.78	4.359	2.457	1.686	6.039	3.799	5.33	2.120	1.920	1.450	3.299	16.068	1.623	2.42	41.79	27.18	322.00	227.000	215.00	723	377	13.47	102.00	2.713	38.246	13.657	7.783	43.646	118.000	22.35	66.76	0.39	83.29	2184.00
30	6	8	67	older	Male	Low	3	No	Yes	BTT	HVAD	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	10.69	8.70	66.212	75.794	2.49	5.509	1.758	1.601	8.508	1.520	13.55	2.120	1.920	1.450	3.108	53.482	1.623	9.74	52.76	108.00	224.00	235.000	349.00	1005	510	19.17	117.00	162.000	146.000	19.099	13.310	43.646	99.662	11.56	86.63	0.39	112.00	1595.00
31	6	14	67	older	Male	Low	3	No	Yes	BTT	HVAD	Alive s/p OHT	alive	182319	40475	22.20	96.28	43.36	11.77	69.34	30.66	1.59	0.89	52.64	27.32	18.56	1.48	22.79	2.88	28.61	75.47	0.33	14.54	0.06	95.90	53.66	7.81	3.27	21.04	1.57	1.50	0.72	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
32	7	0	65	older	Female	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	1073919	336538	31.34	100.00	63.29	14.80	93.05	6.95	0.73	8.05	75.49	4.55	19.16	0.80	7.25	8.78	28.04	88.72	2.51	5.18	1.02	90.49	68.18	2.32	4.09	1.69	3.48	1.27	5.78	103.00	34.40	73.796	17.487	330.00	74.260	18.401	16.683	51.466	82.515	12.80	150.000	15.178	15.208	9.970	26.235	21.107	8.44	133.00	39.26	415.00	111.000	298.00	443	296	138.00	125.00	10.503	37.240	39.468	13.629	111.000	409.000	77.43	78.89	10.95	301.00	5998.00
33	7	1	65	older	Female	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	270301	97229	35.97	91.63	18.43	61.67	97.68	2.32	0.20	5.85	69.45	1.63	28.19	0.73	27.57	21.43	17.05	84.32	7.26	2.73	0.84	99.44	97.60	0.54	2.64	1.51	3.63	0.29	3.40	16.38	6.78	46.507	34.398	28.79	11.752	0.814	4.999	1481.000	8.712	8.92	17.769	2.325	4.279	2.360	63.109	16.386	5.90	34.66	152.00	295.00	55.059	278.00	1037	409	21.48	40.32	6.177	30.898	20.452	2.676	177.000	224.000	36.40	47.22	0.92	160.00	5604.00
34	7	3	65	older	Female	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	499016	78019	15.63	89.41	25.83	51.38	92.98	7.02	0.32	4.66	51.34	6.31	41.59	0.76	36.49	5.96	22.81	73.48	6.73	7.93	0.33	98.78	96.52	1.31	2.06	5.19	2.68	0.59	2.45	16.38	6.52	31.962	18.537	34.49	16.410	2.113	4.287	78.861	9.272	9.04	17.769	2.902	2.861	3.595	11.983	22.669	9.71	14.43	38.26	183.00	56.470	219.00	307	219	29.76	47.34	5.053	3.891	7.570	2.878	33.464	191.000	16.92	81.03	0.92	99.54	4583.00
35	7	8	65	older	Female	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	115825	32513	28.07	91.04	44.61	15.76	80.52	19.48	4.16	7.82	49.91	18.37	30.52	1.20	14.87	3.33	30.19	61.08	8.70	12.56	1.58	89.80	76.71	2.87	6.07	11.12	8.08	1.59	1.17	37.61	21.91	70.846	24.832	57.61	38.448	5.988	8.685	44.610	13.488	17.95	55.857	7.772	7.570	7.238	14.443	23.376	10.40	81.88	77.46	332.00	132.000	332.00	1453	632	38.47	104.00	12.646	40.206	31.261	7.509	79.659	354.000	26.71	180.00	0.92	274.00	7156.00
36	7	21	65	older	Female	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	407080	97651	23.99	91.29	66.96	12.20	84.66	15.34	2.88	5.60	61.89	13.73	22.65	1.74	20.16	7.23	29.39	68.49	5.28	10.60	1.17	92.50	54.35	3.48	6.53	9.61	8.34	1.85	2.83	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
37	8	0	43	younger	Male	High	2	Yes	No	BTT	HMII	Alive s/p OHT	alive	515760	266115	51.60	96.30	69.66	8.57	72.22	27.78	6.26	4.77	43.09	26.01	28.26	2.63	16.67	2.67	28.25	50.35	0.41	12.78	4.47	84.76	19.47	9.56	4.55	8.29	0.87	2.18	0.75	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
38	8	8	43	younger	Male	High	2	Yes	No	BTT	HMII	Alive s/p OHT	alive	539521	223872	41.49	96.72	60.02	12.63	80.13	19.87	1.66	2.48	43.56	19.52	35.24	1.68	20.46	3.67	20.54	55.76	0.18	10.80	2.43	87.82	13.25	6.55	5.96	4.65	0.95	2.51	0.65	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
39	8	21	43	younger	Male	High	2	Yes	No	BTT	HMII	Alive s/p OHT	alive	564317	216312	38.33	97.40	55.80	4.83	74.19	25.81	5.35	5.77	37.90	25.72	34.76	1.62	17.99	2.18	31.75	61.61	0.77	13.44	6.97	76.55	6.97	10.34	11.14	4.46	0.94	5.14	2.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
40	9	0	49	younger	Male	Low	3	No	No	BTT	HVAD	Alive s/p OHT	alive	89251	65181	73.03	76.76	84.53	1.15	74.65	25.35	1.91	1.39	55.03	22.40	19.62	2.95	15.65	6.80	25.85	70.49	0.87	23.26	0.23	83.16	25.00	8.85	5.03	11.63	1.56	2.95	3.08	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
41	9	3	49	younger	Male	Low	3	No	No	BTT	HVAD	Alive s/p OHT	alive	375167	234817	62.59	98.46	60.29	10.18	87.72	12.28	0.46	0.48	68.54	9.90	19.03	2.54	30.58	19.69	15.54	72.77	0.13	20.35	0.03	92.56	36.28	3.75	2.70	9.17	1.19	1.27	0.63	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
42	9	5	49	younger	Male	Low	3	No	No	BTT	HVAD	Alive s/p OHT	alive	256306	162176	63.27	93.82	68.43	8.45	85.45	14.55	0.70	0.53	71.99	11.98	13.37	2.66	32.41	17.96	12.46	69.74	0.41	24.63	0.02	95.72	44.12	4.47	2.23	12.99	1.30	1.20	0.77	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
43	9	8	49	younger	Male	Low	3	No	No	BTT	HVAD	Alive s/p OHT	alive	463136	330714	71.41	98.79	70.51	7.30	87.51	12.49	1.78	0.58	69.60	10.61	17.82	1.97	29.83	14.55	12.59	75.03	0.15	21.61	0.07	93.59	30.94	4.05	3.07	10.00	1.08	1.35	0.62	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
44	10	0	43	younger	Male	Low	3	No	No	BTT	HMII	Alive s/p OHT	alive	92221	27988	30.35	85.45	55.78	1.51	85.56	14.44	0.28	0.28	55.28	4.72	39.72	0.28	0.00	1.89	1.89	89.44	0.00	2.78	0.00	15.00	0.00	3.33	0.83	0.28	0.28	3.33	0.00	7.64	8.05	12.160	18.210	2.65	2.810	2.640	2.960	12.830	1.500	2.82	2.930	1.340	0.600	2.050	88.780	4.370	4.15	28.83	39.26	873.00	84.790	386.00	413	361	15.64	102.00	3.080	25.630	12.440	3.660	40.120	112.000	84.03	48.41	2.75	67.22	13502.00
45	10	1	43	younger	Male	Low	3	No	No	BTT	HMII	Alive s/p OHT	alive	273746	46755	17.08	93.25	50.14	7.57	88.27	11.73	0.09	2.12	54.52	3.36	42.00	0.12	8.40	6.87	0.00	92.00	0.12	3.45	0.00	3.94	11.43	1.12	0.64	2.76	0.33	2.15	0.00	1.74	3.20	8.390	13.870	2.65	2.810	2.640	2.960	5.480	1.500	2.63	1.040	0.420	0.020	0.960	56.230	6.110	2.78	12.80	22.38	257.00	109.000	212.00	207	352	2.90	59.32	3.080	10.480	4.350	2.300	35.160	31.280	16.68	40.19	1.22	37.18	3646.00
46	10	3	43	younger	Male	Low	3	No	No	BTT	HMII	Alive s/p OHT	alive	156053	71752	45.98	78.38	35.43	27.12	78.57	21.43	0.24	0.60	58.56	5.80	18.33	17.31	12.67	72.96	0.53	94.06	0.49	5.11	0.28	22.79	27.47	0.81	5.53	0.01	0.29	0.19	0.00	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
47	10	5	43	younger	Male	Low	3	No	No	BTT	HMII	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	28.26	29.24	25.930	19.130	12.01	9.860	4.910	2.960	12.270	2.300	8.31	8.480	4.420	6.460	5.600	31.690	12.300	6.41	65.76	22.02	605.00	111.000	650.00	279	320	27.98	245.00	8.330	34.600	26.450	10.410	112.000	184.000	48.97	197.00	24.56	128.00	8387.00
48	10	8	43	younger	Male	Low	3	No	No	BTT	HMII	Alive s/p OHT	alive	156053	71752	45.98	78.38	35.43	27.12	78.57	21.43	0.24	0.60	58.56	5.80	18.33	17.31	12.67	72.96	0.53	94.06	0.49	5.11	0.28	22.79	27.47	0.81	5.53	0.01	0.29	0.19	0.00	70.58	65.90	45.390	26.340	33.60	24.140	10.790	5.940	18.220	4.580	16.86	22.040	10.800	17.200	14.210	33.990	19.940	11.61	146.00	23.83	661.00	105.000	765.00	325	270	45.41	414.00	17.810	51.840	49.970	22.030	205.000	266.000	76.21	266.00	48.57	276.00	9997.00
49	11	3	60	younger	Female	High	2	Yes	Yes	BTT	PVAD	Died	dead	116462	41076	35.27	95.44	33.79	25.24	84.51	15.49	2.92	0.99	45.34	13.19	39.12	2.36	17.56	8.75	19.73	79.35	5.17	10.80	0.71	90.04	29.59	10.16	6.81	8.21	3.37	4.86	4.47	16.71	5.07	550.000	96.245	2.72	2.500	6.486	1.361	62.319	3.478	19.00	32.659	1.350	3.356	1.533	254.000	17.441	6.13	35.07	87.82	55.52	228.000	423.00	7084	1125	10.10	84.06	30.293	26.765	20.856	5.302	78.434	153.000	3.21	132.00	1.92	103.00	710.00
50	11	5	60	younger	Female	High	2	Yes	Yes	BTT	PVAD	Died	dead	222650	85649	38.47	98.40	32.09	19.82	77.54	22.46	5.02	1.10	43.96	19.20	33.50	3.34	17.47	9.77	15.63	71.89	6.68	14.08	0.70	86.49	27.87	13.04	9.84	12.30	4.35	7.98	6.39	9.78	8.45	727.000	112.000	2.72	2.500	7.387	1.361	70.374	2.483	35.08	18.389	1.350	3.356	1.378	367.000	16.270	7.93	41.30	108.00	183.00	241.000	526.00	5632	1295	12.58	77.86	46.047	25.264	23.381	4.874	96.343	142.000	4.47	197.00	1.92	79.15	769.00
51	11	8	60	younger	Female	High	2	Yes	Yes	BTT	PVAD	Died	dead	102274	63057	61.65	97.73	27.89	20.66	73.75	26.25	7.33	3.45	35.79	21.92	37.73	4.56	9.97	8.20	16.96	63.96	2.40	11.16	3.07	73.97	5.92	16.69	17.77	11.74	4.90	12.19	6.09	8.08	5.07	592.000	95.220	2.81	2.500	9.032	1.270	88.002	2.970	30.12	13.849	1.350	2.217	1.612	342.000	16.660	5.71	31.97	90.34	159.00	194.000	472.00	4304	1610	4.14	95.92	35.248	54.219	22.115	4.660	88.759	110.000	2.67	143.00	1.92	115.00	702.00
52	11	14	60	younger	Female	High	2	Yes	Yes	BTT	PVAD	Died	dead	133530	81665	61.16	96.46	22.03	16.15	35.30	64.70	13.74	2.33	8.48	52.28	26.37	12.87	5.29	5.79	19.15	47.48	5.65	17.83	5.43	39.88	6.08	52.08	62.11	37.45	21.95	50.15	3.84	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
53	12	0	36	younger	Male	High	2	Yes	NA	BTT	PVAD	Alive s/p OHT	alive	481928	162052	33.63	99.70	64.73	5.05	3.91	96.09	8.89	1.34	0.72	94.99	3.14	1.15	4.07	0.84	0.32	90.28	2.84	1.58	1.27	6.82	9.17	14.61	90.11	4.95	5.71	13.01	0.51	13.27	17.11	204.000	22.584	83.74	2.970	7.222	1.989	113.000	2.290	35.01	208.000	1.165	5.160	2.360	23.895	10.191	3.42	54.53	61.90	250.00	205.000	697.00	388	201	40.18	50.81	11.088	68.713	35.373	1.364	48.096	767.000	84.43	69.91	190.00	798.00	3051.00
54	12	1	36	younger	Male	High	2	Yes	NA	BTT	PVAD	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.77	9.96	64.103	28.423	5.76	2.970	0.420	1.989	477.000	2.290	13.19	3.190	0.940	0.850	2.360	85.133	16.242	2.59	30.46	156.00	455.00	67.295	507.00	225	483	19.92	62.77	6.118	35.321	8.503	1.901	237.000	258.000	31.01	36.77	18.08	131.00	4568.00
55	12	3	36	younger	Male	High	2	Yes	NA	BTT	PVAD	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	43.64	27.96	177.000	51.350	85.07	5.509	4.877	4.159	33.230	9.696	23.76	162.000	3.963	10.605	3.492	26.609	14.056	6.09	99.90	81.00	1152.00	204.000	307.00	854	1019	63.53	142.00	23.341	95.130	65.342	9.657	93.343	633.000	79.44	262.00	147.00	284.00	5911.00
56	12	5	36	younger	Male	High	2	Yes	NA	BTT	PVAD	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7.16	6.78	8.658	17.038	2.80	2.970	1.553	1.231	28.927	3.330	3.48	4.260	1.165	2.060	2.360	30.095	2.770	5.31	49.41	39.91	376.00	64.661	398.00	334	344	20.45	40.32	3.050	12.574	11.877	1.901	18.625	158.000	40.18	183.00	0.92	74.91	10443.75
57	12	8	36	younger	Male	High	2	Yes	NA	BTT	PVAD	Alive s/p OHT	alive	38113	21486	56.37	97.86	58.44	7.88	20.47	79.53	8.09	1.69	1.21	75.00	18.96	4.83	10.07	2.59	0.53	82.19	2.29	9.00	3.59	15.16	0.00	23.07	63.95	4.77	6.52	16.91	0.40	7.16	9.15	39.545	15.389	5.76	2.970	1.399	1.814	11.147	2.723	8.55	7.477	1.430	0.850	2.360	15.487	3.399	2.59	37.42	29.78	548.00	107.000	502.00	225	445	25.83	57.69	3.617	19.015	22.505	1.717	40.247	340.000	56.01	128.00	18.84	96.76	10443.75
58	12	21	36	younger	Male	High	2	Yes	NA	BTT	PVAD	Alive s/p OHT	alive	80991	19210	23.72	99.00	50.61	3.50	12.33	87.67	14.74	4.81	1.05	85.41	11.13	2.41	8.48	0.52	1.56	80.00	8.27	3.76	7.41	13.68	0.00	30.83	77.74	10.83	14.14	19.85	2.81	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
59	13	0	61	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	411616	119210	28.96	90.82	73.63	5.88	74.30	25.70	2.80	6.46	57.94	19.20	18.72	4.13	19.10	15.42	15.30	53.51	13.78	25.19	0.21	47.23	53.77	5.75	5.92	22.63	3.46	2.55	0.74	74.33	630.00	1198.000	29.033	285.00	6.710	41.890	282.000	58.746	108.000	242.00	1363.000	17.372	74.166	4.089	381.000	11.334	76.55	183.00	145.00	703.00	339.000	938.00	642	424	191.00	439.00	51.380	409.000	169.000	31.768	254.000	3217.000	299.00	1084.00	546.00	877.00	6887.00
60	13	1	61	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	430509	79492	18.46	89.31	52.10	20.45	87.13	12.87	1.23	4.76	58.11	9.15	30.55	2.20	29.86	18.05	10.37	63.63	10.23	17.43	0.06	74.05	72.50	2.36	2.98	10.68	1.72	1.03	2.89	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
61	13	3	61	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	395713	119670	30.24	97.90	23.79	6.27	82.17	17.83	0.79	1.99	49.95	13.01	33.91	3.13	28.36	16.83	5.00	56.33	7.13	26.73	0.02	79.50	82.88	1.96	5.55	16.30	3.61	1.88	0.42	4.28	159.00	409.000	41.124	12.27	2.320	4.307	20.356	15.581	16.793	121.00	61.854	2.899	4.794	1.980	49.157	20.020	28.46	32.56	102.00	524.00	415.000	305.00	282	465	41.67	203.00	12.431	121.000	35.967	2.120	73.622	945.000	39.60	451.00	55.87	159.00	676.00
62	13	8	61	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	329840	73468	22.27	98.63	70.61	3.73	51.50	48.50	8.32	3.77	33.46	41.29	20.92	4.33	25.19	5.89	24.07	46.40	3.73	34.64	0.07	64.14	28.43	10.35	9.61	38.78	4.62	5.55	2.91	100.00	430.00	1063.000	57.719	245.00	135.000	21.182	84.771	41.826	160.000	229.00	580.000	22.030	72.412	10.670	229.000	25.806	54.08	223.00	143.00	966.00	310.000	777.00	1415	796	217.00	325.00	45.047	411.000	137.000	44.518	266.000	2406.000	193.00	908.00	321.00	732.00	7464.00
63	13	14	61	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	350294	88823	25.36	97.63	74.37	4.37	73.35	26.65	4.91	4.83	56.54	21.32	19.84	2.30	34.68	11.25	18.37	61.98	5.78	20.55	0.04	63.91	47.54	6.81	6.17	21.77	2.90	2.74	3.00	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
64	14	0	38	younger	Male	High	2	Yes	Yes	BTT	TAH	Died s/p OHT	dead	128451	60927	47.43	96.82	60.77	4.70	77.26	22.74	2.17	1.95	55.58	18.40	21.54	4.47	27.86	18.89	15.33	69.90	0.94	24.40	0.14	70.73	1.85	8.01	8.66	20.21	5.45	3.32	0.39	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
65	14	1	38	younger	Male	High	2	Yes	Yes	BTT	TAH	Died s/p OHT	dead	142796	22573	15.81	80.67	47.31	2.28	63.37	36.63	9.16	6.51	26.02	33.49	37.11	3.37	38.13	7.50	13.13	53.25	5.78	26.27	1.57	40.24	0.00	27.71	22.17	29.16	18.80	15.66	8.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
66	14	3	38	younger	Male	High	2	Yes	Yes	BTT	TAH	Died s/p OHT	dead	157167	38008	24.18	93.47	34.86	3.95	69.71	30.29	3.92	3.92	33.64	26.30	35.78	4.28	39.54	11.49	14.94	66.86	2.85	24.31	1.04	64.50	7.27	21.60	20.53	27.66	14.54	14.18	2.12	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
67	14	5	38	younger	Male	High	2	Yes	Yes	BTT	TAH	Died s/p OHT	dead	121823	23097	18.96	93.48	2.51	2.87	99.52	0.48	0.00	0.00	0.97	0.48	98.55	0.00	50.00	25.00	25.00	94.35	0.00	2.58	0.00	0.16	0.00	17.45	87.40	0.00	0.16	18.26	0.00	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
68	14	8	38	younger	Male	High	2	Yes	Yes	BTT	TAH	Died s/p OHT	dead	196515	88579	45.07	85.54	17.17	3.28	59.38	40.62	3.21	1.41	35.44	35.28	23.87	5.42	62.85	12.32	3.71	66.05	4.38	23.22	0.27	74.53	31.43	25.51	28.12	39.78	25.79	21.57	0.79	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
69	15	0	61	older	Male	High	1	Yes	No	BTT	TAH	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.18	33.63	188.000	37.494	2.31	5.509	0.910	1.590	29.263	1.605	50.08	10.790	1.920	1.782	1.980	112.000	13.845	6.25	8.93	62.53	69.30	213.000	403.00	1373	458	5.90	36.01	7.766	139.000	6.036	0.944	37.403	629.000	9.39	257.00	0.39	40.05	19.79
70	15	1	61	older	Male	High	1	Yes	No	BTT	TAH	Alive s/p OHT	alive	87827	15751	17.93	76.38	31.60	12.17	77.46	22.54	6.35	2.53	50.34	20.22	27.05	2.39	19.68	7.62	19.05	75.55	7.38	9.49	0.71	94.26	89.19	11.68	17.96	18.78	6.97	10.86	12.46	10.69	5.04	118.000	49.561	3.51	52.670	1.125	2.413	349.000	3.799	22.94	23.573	1.950	1.782	2.042	443.000	22.377	3.62	41.79	165.00	206.00	137.000	323.00	1573	1461	10.05	112.00	12.371	72.373	20.483	3.987	364.000	322.000	10.50	82.02	0.39	77.29	380.00
71	15	3	61	older	Male	High	1	Yes	No	BTT	TAH	Alive s/p OHT	alive	55539	13608	24.50	79.00	28.02	15.78	81.90	18.10	6.13	2.42	66.92	14.33	14.98	3.77	20.81	17.45	18.12	78.01	7.08	8.61	0.72	94.04	70.73	11.32	16.69	15.21	5.78	10.50	16.39	6.39	3.54	119.000	48.334	2.31	220.000	1.369	1.590	57.616	1.520	28.03	7.706	1.920	1.450	1.980	158.000	19.806	5.94	27.01	132.00	296.00	172.000	322.00	1138	1362	3.98	63.62	6.549	69.626	14.327	3.358	80.780	351.000	5.73	82.02	0.39	40.05	86.64
72	15	5	61	older	Male	High	1	Yes	No	BTT	TAH	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5.33	34.44	200.000	52.572	3.51	474.000	2.603	1.773	79.861	1.605	55.35	7.706	1.920	1.494	2.382	177.000	14.690	7.33	45.46	166.00	572.00	205.000	488.00	1047	1280	10.05	102.00	4.485	119.000	19.099	7.153	161.000	644.000	9.39	221.00	0.39	83.29	162.00
73	15	8	61	older	Male	High	1	Yes	No	BTT	TAH	Alive s/p OHT	alive	295005	36785	12.47	94.58	36.55	9.14	86.32	13.68	2.45	1.82	37.01	12.52	49.18	1.29	16.24	4.71	12.47	8.36	1.73	6.64	0.81	84.78	46.55	12.52	9.28	5.94	1.73	4.47	2.49	6.39	108.00	240.000	53.791	2.31	408.000	2.900	1.686	53.531	2.648	69.91	77.150	3.312	4.794	1.980	165.000	11.334	12.51	30.71	150.00	348.00	270.000	546.00	1549	1547	23.97	71.06	2.980	140.000	20.483	4.619	182.000	735.000	18.04	283.00	0.39	83.29	86.64
74	15	14	61	older	Male	High	1	Yes	No	BTT	TAH	Alive s/p OHT	alive	60695	20471	33.73	94.91	6.93	7.42	83.97	16.03	3.40	0.69	24.36	14.71	59.33	1.60	18.43	5.07	11.52	54.82	2.91	16.10	0.50	82.44	30.00	51.08	13.25	10.41	3.68	11.31	6.06	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
75	16	0	39	younger	Female	Low	3	Yes	No	BTT	HMII	Died post OHT	dead	52016	30920	59.44	95.77	55.57	24.62	58.73	41.27	2.14	0.97	53.01	5.05	5.45	36.50	9.39	81.29	2.88	81.48	5.65	9.02	0.48	97.75	71.83	0.67	22.89	0.34	1.78	0.49	NA	11.66	5.99	5.180	18.210	2.65	2.810	2.640	2.960	2.600	1.500	3.19	3.790	0.750	1.140	1.270	12.840	1.920	2.78	22.29	13.36	205.00	120.000	433.00	852	235	13.38	108.00	17.980	22.860	7.890	3.660	26.920	114.000	27.36	63.49	6.10	47.28	2960.00
76	16	1	39	younger	Female	Low	3	Yes	No	BTT	HMII	Died post OHT	dead	75516	48801	64.62	92.13	52.90	18.64	70.39	29.61	2.92	2.21	62.18	5.13	7.98	24.70	13.79	69.54	3.19	84.14	3.64	6.36	0.41	94.93	43.78	0.57	15.61	0.23	0.91	0.36	NA	6.69	4.54	3.850	13.310	2.65	2.810	2.640	2.960	15.360	1.500	2.89	2.390	0.640	1.550	1.060	51.270	3.890	2.78	23.91	29.12	139.00	53.210	184.00	503	192	13.38	162.00	30.710	25.630	7.270	3.380	58.260	135.000	10.50	52.34	4.60	111.00	1306.00
77	16	3	39	younger	Female	Low	3	Yes	No	BTT	HMII	Died post OHT	dead	250612	130454	52.05	95.75	60.05	3.29	46.04	53.96	1.34	3.89	33.08	32.47	8.29	26.15	3.56	34.61	0.09	76.71	0.39	13.20	0.28	39.79	14.38	2.26	1.56	10.45	2.89	3.04	0.08	22.58	5.49	6.580	15.690	2.77	2.810	2.640	2.960	19.820	1.500	3.96	4.710	1.750	5.060	1.270	242.000	7.410	6.23	43.70	45.47	205.00	139.000	201.00	614	498	27.61	259.00	37.910	41.540	13.790	4.520	90.330	187.000	20.67	91.18	20.95	94.90	1637.00
78	16	5	39	younger	Female	Low	3	Yes	No	BTT	HMII	Died post OHT	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	36.87	8.59	9.500	14.150	5.49	4.340	2.640	2.960	3.860	1.500	4.92	8.120	2.050	5.620	1.480	49.360	5.860	3.77	51.10	19.90	214.00	142.000	261.00	1163	556	27.24	145.00	37.770	41.540	20.750	5.700	54.980	210.000	28.80	99.93	38.44	100.00	3060.00
79	16	8	39	younger	Female	Low	3	Yes	No	BTT	HMII	Died post OHT	dead	212478	120477	56.70	85.12	48.36	12.51	79.57	20.43	1.35	2.14	58.73	12.87	18.44	9.96	5.63	34.32	0.11	86.74	0.41	6.88	0.15	35.30	53.09	1.23	0.75	2.36	1.04	1.47	NA	39.75	7.52	10.260	14.990	6.70	4.900	2.640	3.060	5.960	1.500	5.57	11.870	2.670	6.610	1.480	49.000	5.860	3.86	55.20	17.87	278.00	105.000	428.00	1180	309	30.12	162.00	41.370	49.350	22.170	5.990	67.210	220.000	34.39	108.00	31.81	119.00	3873.00
80	17	0	70	older	Female	High	2	No	NA	DT	HMII	Alive	alive	86135	70306	81.62	93.71	74.50	1.59	61.74	38.26	1.91	2.67	33.97	14.50	26.81	24.71	20.67	62.47	9.03	73.00	2.00	24.71	1.27	20.23	71.43	0.48	12.79	0.38	3.24	0.48	4.60	2.81	7.26	7.290	173.000	2.65	2.810	2.640	2.960	12.550	1.500	4.28	8.850	0.640	0.770	1.160	2.930	3.890	3.28	27.19	22.83	145.00	70.170	765.00	1478	482	9.57	94.83	94.860	103.000	8.520	3.660	18.060	120.000	19.71	40.19	28.19	51.59	1276.00
81	17	1	70	older	Female	High	2	No	NA	DT	HMII	Alive	alive	81004	41334	51.03	94.06	53.64	2.99	61.70	38.30	3.10	2.75	39.07	13.34	22.12	25.47	12.58	63.11	15.99	78.92	2.93	17.38	3.35	23.24	71.88	0.52	11.70	0.09	1.29	0.52	9.83	7.64	4.08	4.170	41.980	2.65	2.810	2.640	2.960	133.000	1.500	2.63	7.760	0.420	1.770	0.910	98.210	8.740	4.54	15.89	77.14	211.00	64.900	374.00	1714	959	5.00	64.16	21.350	54.290	8.520	2.560	284.000	52.100	11.10	21.92	7.15	51.59	1301.00
82	17	3	70	older	Female	High	2	No	NA	DT	HMII	Alive	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.22	3.20	3.220	21.580	2.65	2.810	2.640	2.960	15.210	1.500	2.63	2.520	0.420	0.020	0.680	58.680	7.800	3.77	8.37	31.94	83.16	74.380	388.00	644	432	2.90	49.55	8.660	30.220	4.630	1.790	14.940	45.600	11.69	14.26	1.53	29.96	690.00
83	17	5	70	older	Female	High	2	No	NA	DT	HMII	Alive	alive	90024	39434	43.80	95.32	68.83	5.62	71.59	28.41	0.85	1.66	35.23	13.02	35.80	15.96	22.94	52.37	5.85	76.42	1.14	21.83	0.62	14.54	48.57	0.38	4.69	0.00	0.80	0.38	0.62	0.58	3.63	3.220	25.010	2.65	2.810	2.640	2.960	23.480	1.500	2.63	2.390	0.420	0.100	0.860	31.820	7.670	11.61	15.89	41.91	454.00	91.330	506.00	670	421	3.21	54.44	14.370	37.430	6.660	2.300	33.500	58.260	15.08	19.42	6.10	41.52	1837.00
84	17	8	70	older	Female	High	2	No	NA	DT	HMII	Alive	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	37.61	21.91	44.071	23.633	39.20	9.532	6.428	5.102	32.860	7.062	12.93	20.414	2.167	3.138	5.978	41.851	8.190	4.95	63.21	39.70	328.00	175.000	323.00	808	540	34.88	91.67	13.604	48.125	30.231	5.864	59.949	466.000	81.74	150.00	4.51	195.00	9741.00
85	17	14	70	older	Female	High	2	No	NA	DT	HMII	Alive	alive	106791	80532	75.41	88.82	79.92	1.57	62.32	37.68	1.70	4.02	28.04	22.86	34.11	15.00	23.92	39.41	15.03	61.61	3.66	34.55	1.97	20.36	73.33	0.80	8.57	0.27	1.79	0.98	0.71	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
86	18	0	63	older	Male	High	1	No	NA	BTT	HMII	Died	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	60.38	32.81	186.000	38.833	32.98	21.709	3.051	5.621	37.885	11.091	29.33	189.000	6.338	16.504	4.089	278.000	17.668	7.17	59.99	54.99	695.00	232.000	211.00	828	1145	50.86	107.00	10.251	111.000	31.722	7.153	120.000	533.000	28.75	322.00	2.34	224.00	3524.00
87	18	1	63	older	Male	High	1	No	NA	BTT	HMII	Died	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	63.95	40.14	249.000	28.453	32.98	21.709	3.357	4.711	28.562	11.091	38.39	220.000	6.089	13.032	4.089	124.000	15.114	5.01	59.99	41.74	638.00	289.000	303.00	509	713	59.63	121.00	9.867	123.000	56.352	7.153	82.196	685.000	46.72	384.00	14.39	229.00	5489.00
88	18	3	63	older	Male	High	1	No	NA	BTT	HMII	Died	dead	366713	77282	21.07	98.61	66.55	12.44	93.65	6.35	0.53	0.60	62.22	4.68	32.38	0.72	19.94	9.89	28.23	83.18	0.51	14.38	0.30	94.72	54.39	2.07	11.33	2.86	3.40	1.48	3.58	65.71	24.75	198.000	26.329	40.68	18.837	4.631	4.711	20.178	9.696	29.66	185.000	5.841	11.411	4.293	71.642	12.166	3.92	74.20	35.60	1110.00	222.000	300.00	489	627	50.86	126.00	9.479	104.000	51.467	10.275	77.933	727.000	62.42	422.00	11.03	219.00	9557.42
89	18	5	63	older	Male	High	1	No	NA	BTT	HMII	Died	dead	262251	55344	21.10	99.55	62.37	14.39	93.00	7.00	1.03	0.69	65.71	4.74	28.37	1.17	25.93	17.28	25.93	79.87	1.20	17.05	0.48	97.76	58.18	2.27	11.13	4.12	4.91	1.87	6.10	89.92	70.33	309.000	36.344	103.00	29.010	8.732	7.037	28.329	24.712	57.19	384.000	8.403	25.681	5.347	93.592	18.950	6.40	67.14	51.06	434.00	238.000	328.00	896	536	91.53	134.00	14.161	155.000	92.272	10.890	115.000	873.000	64.87	398.00	65.30	368.00	2247.00
90	18	8	63	older	Male	High	1	No	NA	BTT	HMII	Died	dead	309193	61091	19.76	98.15	56.62	7.24	83.61	16.39	3.27	1.52	58.03	11.90	28.13	1.93	24.65	11.36	1.52	79.53	1.45	13.33	0.97	92.63	25.76	3.50	9.23	7.57	3.73	3.48	0.00	60.38	17.64	183.000	34.233	42.23	20.268	6.645	4.488	29.029	11.795	27.05	177.000	6.089	10.605	3.887	154.000	12.585	3.77	56.39	56.34	1534.00	165.000	446.00	487	478	53.19	121.00	10.759	85.242	37.382	13.310	79.359	700.000	104.00	490.00	12.72	151.00	9557.42
91	19	0	68	older	Male	Low	3	No	NA	DT	HMII	Died	dead	147997	54649	36.93	91.75	79.63	7.62	74.86	25.14	0.60	3.45	62.45	22.87	12.30	2.38	45.98	9.28	13.61	74.33	2.67	21.19	0.84	91.31	90.15	3.58	2.25	21.79	1.36	0.58	0.34	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
92	19	1	68	older	Male	Low	3	No	NA	DT	HMII	Died	dead	177687	56400	31.74	97.13	69.76	12.60	81.83	18.17	0.65	1.61	55.77	16.13	26.02	2.09	53.19	10.70	11.57	82.92	1.38	13.98	0.28	89.83	85.59	3.78	1.85	14.65	1.30	0.68	0.18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93	19	3	68	older	Male	Low	3	No	NA	DT	HMII	Died	dead	233155	67140	28.80	98.44	74.89	5.32	81.96	18.04	1.00	1.88	49.03	16.59	32.90	1.48	49.92	7.20	12.83	83.44	1.39	12.98	0.14	89.70	69.70	4.58	1.62	13.89	1.08	1.08	0.17	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
94	19	5	68	older	Male	Low	3	No	NA	DT	HMII	Died	dead	99257	29455	29.68	97.33	62.48	4.73	80.60	19.40	1.84	1.25	45.06	18.36	35.25	1.33	44.61	6.32	12.27	82.89	0.88	13.20	0.09	83.55	35.29	7.23	3.54	14.75	2.29	3.02	1.20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
95	19	8	68	older	Male	Low	3	No	NA	DT	HMII	Died	dead	76358	18702	24.49	97.32	29.48	11.10	86.29	13.71	2.43	0.50	37.92	12.13	48.32	1.63	46.45	11.70	11.35	83.96	0.20	10.50	0.06	57.82	50.00	9.95	29.60	10.20	0.64	9.36	0.80	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
96	20	0	37	younger	Female	High	2	Yes	NA	BTT	PVAD	Died s/p OHT	dead	56374	12141	21.54	67.38	27.12	3.32	51.84	48.16	1.84	1.84	2.94	45.59	47.79	3.68	0.00	0.00	2.29	92.65	0.37	5.51	0.72	30.15	60.00	6.99	9.56	0.37	0.00	3.68	0.00	2.22	9.41	292.000	21.883	2.31	2.320	1.245	1.590	7.255	1.520	70.36	2.120	1.920	1.450	1.980	90.424	1.623	3.92	8.93	91.17	78.30	141.000	579.00	666	562	10.05	55.48	11.010	72.373	9.732	2.120	40.533	733.000	2.89	164.00	0.39	33.64	518.00
97	20	1	37	younger	Female	High	2	Yes	NA	BTT	PVAD	Died s/p OHT	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7.16	11.35	124.000	35.144	2.80	2.970	0.552	1.231	25.459	2.290	28.30	3.190	0.940	0.850	2.360	219.000	2.770	10.35	40.14	133.00	229.00	86.923	599.00	801	488	22.49	26.21	38.398	66.074	17.389	2.089	52.042	488.000	5.07	124.00	0.92	59.20	1144.00
98	20	3	37	younger	Female	High	2	Yes	NA	BTT	PVAD	Died s/p OHT	dead	7018	3464	49.36	89.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	8.66	13.04	89.555	29.993	4.95	2.970	1.247	1.474	18.328	2.432	24.88	3.190	2.486	0.850	2.593	45.524	4.181	8.44	60.77	134.00	181.00	94.833	598.00	549	407	18.84	57.69	110.000	103.000	18.917	3.601	45.144	420.000	10.11	113.00	0.92	54.13	1900.00
99	20	5	37	younger	Female	High	2	Yes	NA	BTT	PVAD	Died s/p OHT	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	10.69	16.87	180.000	45.496	4.61	2.320	1.825	1.686	21.569	2.111	48.20	2.120	1.920	1.450	2.382	148.000	2.224	14.68	41.79	117.00	278.00	137.000	520.00	821	424	13.47	107.00	34.287	84.751	13.657	6.521	58.902	618.000	8.24	146.00	0.39	65.11	1301.00
100	20	8	37	younger	Female	High	2	Yes	NA	BTT	PVAD	Died s/p OHT	dead	80210	21828	27.21	85.74	20.38	3.50	61.98	38.02	8.55	2.90	5.50	34.96	56.18	3.36	0.00	0.00	3.21	89.31	0.61	2.60	1.49	23.66	0.00	21.37	9.16	1.07	0.15	5.34	1.31	132.00	18.42	230.000	38.833	20.88	7.954	3.203	8.499	16.494	4.406	56.12	2.120	26.710	15.890	5.564	79.472	18.095	17.57	118.00	101.00	250.00	163.000	563.00	804	352	72.23	160.00	30.736	184.000	43.034	13.310	76.501	902.000	49.14	176.00	58.43	277.00	6159.00
101	20	14	37	younger	Female	High	2	Yes	NA	BTT	PVAD	Died s/p OHT	dead	55068	19145	34.77	75.16	16.90	3.30	64.00	36.00	4.21	2.11	10.53	34.11	52.42	2.95	0.00	0.00	2.94	92.42	0.00	3.37	1.00	22.74	0.00	12.42	11.16	0.63	0.00	4.63	0.00	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
102	20	21	37	younger	Female	High	2	Yes	NA	BTT	PVAD	Died s/p OHT	dead	76673	23615	30.80	83.39	26.27	2.06	63.55	36.45	5.42	5.42	5.17	34.48	58.13	2.22	0.00	0.00	2.72	89.41	0.00	3.45	1.95	18.23	4.55	18.23	8.87	1.23	0.25	4.68	0.00	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
103	21	0	46	younger	Male	High	2	No	NA	DT	HMII	Alive	alive	226762	25868	11.41	89.41	25.31	12.05	55.06	44.94	11.31	2.69	14.79	42.57	39.95	2.69	19.87	3.88	32.88	68.95	10.48	13.57	0.59	80.62	22.67	31.91	19.38	15.58	10.37	16.55	8.18	2.18	2.45	60.794	34.617	2.31	2.320	1.245	1.590	68.832	1.520	7.45	2.120	1.920	1.450	1.980	130.000	3.544	14.61	5.57	47.27	442.00	52.814	249.00	328	245	3.98	55.48	2.230	19.937	2.737	3.987	21.566	118.000	2.89	23.91	0.39	52.69	1293.00
104	21	3	46	younger	Male	High	2	No	NA	DT	HMII	Alive	alive	244049	39386	16.14	94.94	40.46	13.23	40.93	59.07	13.36	3.17	8.33	54.90	32.28	4.49	18.52	4.21	23.83	55.18	13.87	19.39	1.44	70.53	35.67	33.62	33.47	22.86	15.26	25.81	5.11	2.18	2.71	17.955	26.715	9.57	2.320	1.893	1.590	3.759	1.520	3.89	2.120	1.920	1.450	1.980	59.228	1.623	7.17	8.93	60.31	820.00	73.754	165.00	347	262	3.98	63.62	2.230	18.052	2.737	3.358	15.234	90.169	11.56	77.19	0.39	33.64	2726.00
105	21	5	46	younger	Male	High	2	No	NA	DT	HMII	Alive	alive	344736	250121	72.55	89.83	7.54	3.81	45.84	54.16	3.96	1.53	13.39	52.70	32.18	1.72	67.14	2.92	2.44	74.68	6.81	13.91	0.21	63.33	58.02	33.05	30.94	19.83	13.96	26.74	0.22	2.18	4.12	15.278	24.202	12.27	2.320	2.457	1.590	4.492	1.520	3.07	2.120	1.920	1.450	2.558	43.554	1.623	2.86	23.32	48.13	1082.00	71.954	125.00	260	251	5.90	77.95	2.230	18.052	7.234	7.153	27.925	153.000	29.97	118.00	0.39	52.69	4714.00
106	21	8	46	younger	Male	High	2	No	NA	DT	HMII	Alive	alive	216934	140093	64.58	96.37	4.78	7.22	22.49	77.51	13.49	0.64	7.22	67.61	15.17	10.00	35.88	7.82	17.22	51.28	39.40	6.56	1.33	62.59	58.06	66.67	64.59	53.00	50.26	63.78	4.97	2.18	2.45	24.797	23.042	8.26	2.320	1.625	1.590	3.060	1.520	4.03	2.120	1.920	1.450	1.980	44.329	0.827	2.13	5.57	54.35	370.00	84.966	89.52	348	255	3.98	46.41	2.230	9.990	2.520	2.120	9.035	55.097	2.89	23.91	0.39	27.20	1113.00
107	21	14	46	younger	Male	High	2	No	NA	DT	HMII	Alive	alive	215820	66785	30.94	92.54	17.20	6.01	25.88	74.12	9.15	1.45	3.09	71.72	22.44	2.74	65.43	3.42	0.04	69.38	15.93	8.85	0.74	50.58	24.07	52.78	48.88	32.28	28.19	44.04	0.04	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
108	21	21	46	younger	Male	High	2	No	NA	DT	HMII	Alive	alive	670692	523014	77.98	96.05	5.07	2.64	23.61	76.39	4.11	0.49	4.79	73.53	18.68	3.00	42.40	2.72	4.49	71.95	20.81	5.51	0.45	48.22	44.62	60.13	60.26	39.07	36.82	56.38	1.49	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
109	22	0	75	older	Male	High	2	No	NA	DT	HMII	Alive	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	59.97	33.76	44.886	35.293	48.50	30.282	52.148	11.702	46.900	18.224	10.78	72.616	5.569	5.458	11.016	23.895	17.965	7.15	143.00	43.59	989.00	135.000	584.00	662	925	40.52	163.00	13.354	43.750	59.591	13.629	94.278	449.000	104.00	196.00	0.92	248.00	10443.75
110	22	1	75	older	Male	High	2	No	NA	DT	HMII	Alive	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6.42	13.61	24.600	30.292	4.17	3.956	10.044	3.491	27.540	3.964	5.69	3.190	0.940	1.805	2.360	69.905	14.225	3.22	64.43	56.18	263.00	47.791	495.00	624	1321	16.57	80.75	6.352	20.019	15.869	3.706	57.971	119.000	6.20	184.00	0.92	49.17	2297.00
111	22	3	75	older	Male	High	2	No	NA	DT	HMII	Alive	alive	121794	28129	23.10	97.05	52.79	16.42	70.08	29.92	0.29	0.54	50.71	21.46	22.96	4.86	34.18	12.51	23.49	39.78	0.31	55.18	0.35	96.01	16.67	1.74	5.27	23.38	2.95	1.00	0.10	2.77	13.90	16.630	13.668	9.16	2.970	3.372	2.078	10.277	6.347	2.66	4.260	1.165	1.090	2.360	36.910	7.340	2.59	60.77	27.10	698.00	100.000	514.00	323	606	25.83	54.27	4.276	23.867	33.318	4.024	31.548	264.000	61.66	124.00	0.92	69.60	10443.75
112	22	5	75	older	Male	High	2	No	NA	DT	HMII	Alive	alive	82157	17295	21.05	99.20	72.54	7.41	68.21	31.79	1.10	0.55	48.47	22.90	23.60	5.04	31.90	13.57	18.10	37.84	0.47	56.73	0.35	92.37	14.29	2.05	7.79	26.20	4.72	1.42	1.02	17.15	11.58	21.356	25.652	9.57	5.509	5.963	3.002	17.410	6.315	3.89	23.573	2.504	1.494	5.132	87.134	12.166	2.86	56.39	33.82	845.00	150.000	484.00	556	814	19.17	130.00	6.685	27.906	20.483	10.890	40.533	185.000	70.35	99.39	2.34	118.00	7899.00
113	22	8	75	older	Male	High	2	No	NA	DT	HMII	Alive	alive	154810	27067	17.48	98.04	72.18	5.03	74.91	25.09	0.90	1.65	58.05	14.76	20.82	6.37	23.43	25.71	22.57	44.94	0.60	49.36	0.71	86.59	22.73	3.30	7.64	20.15	4.87	1.72	0.00	10.19	18.30	30.677	25.655	16.47	9.100	6.693	4.896	29.159	3.022	8.07	3.688	1.295	1.557	8.079	68.056	13.360	5.90	58.29	31.79	671.00	47.791	696.00	506	704	18.84	102.00	12.748	17.993	11.877	5.864	44.162	198.000	49.71	115.00	1.39	114.00	10443.75
114	22	21	75	older	Male	High	2	No	NA	DT	HMII	Alive	alive	197936	15446	7.80	95.86	50.35	5.44	79.13	20.87	3.98	3.11	52.30	15.90	29.81	1.99	20.23	5.78	24.86	40.62	1.12	50.06	1.89	62.36	8.00	7.20	9.94	13.17	3.60	5.09	1.55	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
115	23	0	30	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	114586	35996	31.41	100.00	59.03	11.31	75.87	24.13	1.52	7.69	69.83	11.67	10.20	8.30	27.80	29.94	2.04	84.64	0.64	10.54	0.61	91.57	61.34	4.99	7.15	11.57	5.92	2.53	1.92	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
116	23	1	30	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	13.27	4.09	22.842	12.996	2.80	2.970	1.098	0.346	6.097	2.290	3.48	3.190	0.940	0.850	2.360	103.000	4.849	4.64	2.91	28.25	276.00	25.145	449.00	274	442	7.15	31.49	3.050	10.241	2.290	1.036	12.701	13.318	2.92	110.00	0.92	39.59	1621.00
117	23	3	30	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	349054	129419	37.08	95.25	62.35	8.81	79.64	20.36	2.72	4.45	57.70	13.93	25.89	2.48	57.14	13.49	8.69	74.29	3.76	12.35	1.41	85.87	62.53	3.41	5.43	7.67	1.40	1.92	1.06	72.99	13.61	25.476	15.689	9.16	4.312	3.544	2.168	12.912	2.432	6.98	3.190	1.430	0.850	5.769	30.560	4.849	4.20	59.53	23.70	268.00	56.470	507.00	350	496	15.38	108.00	3.050	23.400	13.860	7.400	61.926	158.000	3.85	123.00	0.92	108.00	2374.00
118	23	5	30	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	447309	176789	39.52	93.00	53.57	9.32	80.92	19.08	2.16	5.73	56.72	12.51	28.43	2.34	47.68	12.19	11.00	73.80	4.44	12.53	2.57	83.88	49.09	5.24	6.82	8.32	2.18	2.28	1.72	75.52	39.53	49.720	16.363	80.35	13.585	14.246	8.044	28.002	22.225	15.11	23.132	3.869	4.279	13.928	30.947	13.071	8.12	149.00	16.40	384.00	120.000	681.00	419	400	41.84	179.00	5.172	46.471	46.582	14.665	126.000	364.000	36.57	89.46	22.99	224.00	4716.00
119	23	8	30	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	558290	301468	54.00	95.62	47.73	7.04	79.13	20.87	3.43	7.49	53.19	13.48	30.32	3.01	36.36	14.54	12.10	70.55	5.15	11.89	3.51	79.79	52.89	6.98	8.50	7.82	1.92	3.09	1.96	55.93	9.15	32.389	17.862	2.80	2.970	3.716	1.989	10.711	2.290	7.10	NA	NA	12.466	2.360	17.125	3.019	4.20	57.05	18.04	905.00	265.000	855.00	393	618	22.99	71.07	3.050	22.929	32.290	4.131	50.068	170.000	NA	97.14	17.70	80.29	10443.75
120	23	14	30	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	479402	362845	75.69	97.95	44.30	6.30	76.53	23.47	3.90	11.10	34.99	12.04	50.31	2.66	21.93	14.88	10.05	70.21	4.66	7.31	7.73	49.96	29.38	13.44	17.37	5.64	3.01	6.95	2.28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
121	23	21	30	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	334942	191418	57.15	93.28	70.35	4.04	70.89	29.11	6.20	17.70	56.45	19.57	20.44	3.53	38.91	14.64	10.35	55.11	6.61	14.34	8.70	80.96	58.92	7.58	7.96	10.11	2.08	2.08	2.48	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
122	24	0	67	older	Male	High	2	Yes	NA	BTT	HMII	Died	dead	129980	41600	32.00	91.40	39.03	2.85	49.63	50.37	7.02	11.55	28.37	29.11	20.79	21.72	19.43	28.34	9.80	69.59	4.90	20.33	6.13	31.15	33.60	20.15	22.27	0.28	4.25	16.54	2.09	33.99	28.59	16.040	25.430	12.01	7.920	6.660	3.530	19.960	3.020	7.39	10.910	3.870	5.620	7.350	29.050	11.190	6.77	80.92	26.34	485.00	156.000	684.00	574	531	28.71	233.00	9.210	37.430	28.250	15.220	112.000	158.000	41.01	59.87	50.92	139.00	4188.00
123	24	1	67	older	Male	High	2	Yes	NA	BTT	HMII	Died	dead	59478	20182	33.93	89.07	44.50	10.08	60.93	39.07	2.10	2.70	42.88	13.69	17.66	25.77	13.06	46.12	3.13	81.35	1.16	16.11	0.83	37.58	6.12	5.52	11.64	0.06	1.49	2.76	4.07	4.02	3.20	2.320	19.970	2.65	2.810	2.640	2.960	405.000	1.500	2.63	2.130	0.460	0.450	0.860	99.660	13.000	2.78	12.80	206.00	266.00	84.610	329.00	197	841	3.21	39.72	8.990	27.180	6.360	2.830	565.000	23.210	11.10	10.23	1.22	38.63	1588.00
124	24	3	67	older	Male	High	2	Yes	NA	BTT	HMII	Died	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6.69	9.68	5.520	72.480	2.65	2.810	2.640	2.960	12.830	1.500	3.81	3.490	3.700	1.140	1.870	30.080	8.600	3.57	26.36	120.00	339.00	166.000	633.00	484	808	12.17	90.21	79.330	79.230	12.440	4.520	64.780	75.100	48.83	38.04	18.56	55.88	3892.00
125	24	5	67	older	Male	High	2	Yes	NA	BTT	HMII	Died	dead	26646	9754	36.61	98.69	58.32	7.57	64.88	35.12	2.06	0.55	46.50	12.62	17.70	23.18	15.07	53.31	12.50	83.40	1.10	15.64	1.76	28.12	0.00	7.54	16.46	0.55	1.92	6.04	13.00	0.58	3.20	3.220	34.140	2.65	2.810	2.640	2.960	39.370	1.500	2.63	2.930	0.420	0.020	0.540	66.160	8.470	3.38	5.65	51.46	66.05	106.000	497.00	1069	1138	2.90	29.91	6.510	29.470	5.480	1.550	51.690	2.870	3.21	8.86	43.82	41.52	535.00
126	24	8	67	older	Male	High	2	Yes	NA	BTT	HMII	Died	dead	73641	21944	29.80	96.79	48.91	4.47	62.74	37.26	2.84	3.16	37.47	17.79	24.95	19.79	19.46	38.11	9.46	79.79	1.89	13.58	1.24	27.05	20.00	14.11	13.37	0.21	2.74	10.53	3.31	2.44	4.08	4.340	49.370	2.65	2.810	2.640	2.960	25.260	1.500	2.63	6.360	0.420	0.100	0.770	74.530	10.920	5.20	8.37	91.97	73.49	135.000	493.00	1207	1273	2.90	49.55	17.980	28.710	7.270	2.560	35.160	14.190	3.93	8.86	52.09	35.74	551.00
127	25	0	68	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	44004	20612	46.84	43.65	15.61	21.54	49.64	50.36	10.37	0.57	15.94	45.15	33.18	5.73	24.69	5.68	20.25	44.58	1.55	36.17	0.88	67.23	27.27	32.35	15.48	20.02	4.49	10.94	2.76	71.72	47.35	26.349	37.381	158.00	33.334	10.660	8.044	35.402	46.417	6.87	NA	10.690	8.792	2.398	99.342	9.045	6.48	97.08	46.45	297.00	181.000	191.00	567	222	49.74	80.75	6.059	37.993	25.593	20.817	174.000	399.000	44.78	92.77	41.52	NA	4980.00
128	25	1	68	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	99731	23424	23.49	91.81	41.33	10.89	35.24	64.76	11.83	1.62	18.71	59.21	16.19	5.89	28.16	5.89	18.53	39.98	4.66	42.16	2.33	84.07	2.63	22.55	15.21	40.28	6.07	9.70	3.96	29.43	17.11	20.632	25.281	48.50	22.252	5.900	4.692	222.000	11.168	3.91	72.616	4.422	2.060	2.360	191.000	11.918	7.37	68.02	138.00	324.00	136.000	149.00	347	223	30.17	80.75	4.036	30.061	12.370	7.619	280.000	331.000	21.43	70.73	2.56	274.00	3581.00
129	25	3	68	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	74.26	46.69	17.521	32.607	125.00	34.355	9.340	7.512	18.328	13.879	4.56	190.000	13.031	14.904	2.360	35.125	11.629	8.85	37.42	35.14	218.00	222.000	173.00	1649	290	71.92	85.48	3.557	52.272	44.557	15.485	186.000	514.000	30.19	168.00	48.69	743.00	1283.00
130	25	5	68	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	63.06	27.96	35.932	31.732	44.55	16.008	3.051	5.621	18.791	10.391	5.03	152.000	11.673	14.663	1.980	35.584	10.919	6.56	45.46	42.92	420.00	180.000	216.00	2058	291	95.02	121.00	5.314	63.342	43.034	23.444	164.000	411.000	28.75	257.00	28.67	330.00	1677.00
131	25	8	68	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	129637	50001	38.57	90.65	44.41	6.05	30.66	69.34	17.12	4.05	10.62	66.50	19.60	3.28	26.41	3.46	13.37	39.74	7.15	39.49	11.91	71.97	2.70	32.48	33.14	43.91	7.81	22.70	3.82	40.52	23.75	22.842	21.835	74.36	13.123	5.021	5.931	27.077	9.272	4.67	134.000	12.603	9.710	2.360	53.765	8.759	6.48	60.77	35.38	426.00	223.000	224.00	1195	211	57.75	93.20	3.857	32.945	29.201	15.485	185.000	523.000	35.17	121.00	27.51	602.00	4088.00
132	25	14	68	older	Male	High	2	No	No	BTT	HMII	Alive s/p OHT	alive	134368	46363	34.50	88.32	34.25	4.63	32.47	67.53	12.86	8.38	8.91	64.89	22.88	3.32	24.78	3.89	13.58	36.16	6.01	40.33	13.77	72.11	1.89	29.89	26.36	42.80	6.17	15.97	3.29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
133	26	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	280.00	525.00	894.000	111.000	286.00	113.000	13.198	30.593	9.360	23.275	174.00	653.000	95.232	163.000	1.980	163.000	80.128	16.37	766.00	93.58	660.00	253.000	702.00	389	553	193.00	427.00	39.685	321.000	496.000	9.657	37.403	1784.000	157.00	665.00	295.00	2180.00	6363.00
134	26	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	209.00	380.00	836.000	74.333	164.00	60.004	8.996	20.619	71.052	23.994	124.00	456.000	58.649	117.000	1.980	209.000	71.003	17.17	459.00	118.00	531.00	192.000	679.00	459	769	168.00	407.00	51.595	250.000	307.000	8.411	70.721	1328.000	113.00	567.00	236.00	1572.00	3605.00
135	26	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	288.00	641.00	1098.000	122.000	139.00	111.000	14.282	48.154	80.298	48.404	230.00	657.000	85.433	180.000	1.980	217.000	124.000	41.99	680.00	136.00	787.00	257.000	715.00	735	832	188.00	584.00	73.519	363.000	526.000	11.500	67.799	1782.000	135.00	732.00	335.00	3573.00	6153.00
136	27	0	64	older	Male	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	59881	8971	14.98	97.40	50.06	9.57	75.84	24.16	3.59	8.13	64.83	13.40	15.67	6.10	19.31	18.81	16.83	76.67	6.10	6.82	2.00	88.16	47.06	10.53	11.24	10.41	9.93	6.94	11.93	16.38	67.35	180.000	62.930	127.00	2.970	36.468	4.794	25.690	5.645	46.84	139.000	1.295	17.624	2.360	148.000	9.904	9.46	143.00	64.82	261.00	507.000	809.00	891	476	76.81	136.00	18.740	113.000	98.896	20.817	358.000	925.000	91.47	168.00	123.00	653.00	761.00
137	27	1	64	older	Male	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	60193	23874	39.66	96.72	58.83	16.24	80.37	19.63	1.87	3.31	68.58	10.62	14.72	6.08	21.33	22.83	3.80	80.61	3.63	8.03	0.33	94.16	57.26	2.96	5.01	10.19	4.72	1.41	1.24	3.57	32.49	145.000	59.668	35.43	2.970	12.151	4.287	29.390	5.645	54.69	26.852	0.940	6.660	2.360	142.000	12.494	6.13	91.76	118.00	294.00	354.000	659.00	811	759	35.98	83.92	12.544	90.832	44.557	39.375	223.000	830.000	67.87	147.00	58.83	260.00	528.00
138	27	3	64	older	Male	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	8.66	39.21	135.000	67.453	61.20	3.273	20.290	5.826	23.842	5.819	45.26	64.203	1.569	9.863	3.090	66.315	10.766	5.31	155.00	68.00	355.00	420.000	945.00	1063	603	56.22	136.00	16.032	94.647	49.105	19.468	230.000	804.000	72.71	206.00	83.10	435.00	814.00
139	27	5	64	older	Male	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	135335	27834	20.57	97.35	44.75	5.61	63.22	36.78	8.49	17.63	39.01	23.16	30.72	7.11	17.53	10.38	12.16	42.70	4.93	19.28	4.68	67.37	4.10	26.64	17.76	19.21	14.54	14.47	10.60	2.18	38.51	215.000	76.341	16.50	2.320	10.862	2.224	8.932	1.520	53.96	36.506	1.920	7.437	1.980	105.000	8.874	7.63	77.70	68.16	389.00	204.000	723.00	1321	487	50.26	160.00	12.674	125.000	27.485	45.764	177.000	710.000	55.82	360.00	90.56	239.00	351.00
140	27	8	64	older	Male	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	247113	62042	25.11	96.41	43.93	14.82	78.42	21.58	2.52	5.24	65.35	12.22	17.53	4.90	20.39	17.20	2.93	77.86	2.11	8.78	2.94	84.64	9.70	10.64	13.93	9.90	11.59	8.48	3.51	22.57	29.97	91.301	58.481	24.03	7.004	8.899	5.515	27.077	7.972	25.93	37.523	2.650	3.138	5.142	70.641	12.783	7.59	94.97	56.14	551.00	288.000	717.00	1003	373	39.51	130.00	9.800	75.273	27.655	7.838	145.000	524.000	52.18	137.00	41.89	251.00	338.00
141	27	14	64	older	Male	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	142053	56983	40.11	93.01	62.72	12.87	85.10	14.90	0.69	1.64	68.02	8.40	21.03	2.55	17.03	13.68	1.08	83.33	0.37	7.38	0.26	79.00	16.07	3.01	5.85	5.16	5.24	1.77	0.70	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
142	27	21	64	older	Male	High	2	No	Yes	BTT	HMII	Alive s/p OHT	alive	60384	17029	28.20	93.65	40.96	21.36	83.80	16.20	1.47	2.32	63.52	9.69	23.80	2.99	23.55	15.58	0.91	78.16	1.09	8.13	1.00	83.65	17.72	4.70	8.42	5.99	7.22	3.76	1.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
143	28	0	25	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	47645	37377	78.45	94.61	2.47	9.41	78.56	17.02	1.71	4.63	34.94	16.09	44.77	4.21	4.35	28.99	59.42	67.26	3.10	26.22	7.50	72.82	53.90	13.50	28.20	3.04	7.70	7.67	0.96	9.61	35.14	22.350	20.460	3.79	22.660	3.480	3.530	9.020	3.210	9.75	7.060	1.210	1.550	0.770	20.320	2.980	2.78	20.67	13.18	290.00	37.840	754.00	653	382	20.62	170.00	9.210	42.210	9.160	3.660	54.980	131.000	33.28	78.76	74.92	178.00	1807.00
144	28	1	25	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	87452	43025	49.20	91.54	1.98	26.71	92.06	6.76	0.77	4.67	47.21	4.70	45.17	2.92	6.06	18.18	60.61	81.01	3.95	14.10	12.04	85.97	79.84	5.82	14.15	1.11	4.77	2.84	0.00	12.71	13.67	11.390	16.950	2.77	3.020	2.690	2.960	29.730	1.790	5.89	3.490	1.610	2.220	2.640	103.000	6.370	5.67	25.55	37.67	369.00	23.230	830.00	341	373	18.73	111.00	6.740	23.260	13.120	5.700	76.070	128.000	32.15	77.14	17.37	97.61	3031.00
145	28	3	25	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	124056	80873	65.19	95.41	0.37	14.36	90.47	5.98	0.39	0.60	41.84	4.26	51.08	2.81	18.82	35.29	43.53	86.97	0.10	11.32	1.10	75.49	12.12	3.80	14.94	0.24	0.88	3.25	0.00	37.44	26.01	28.120	25.360	9.28	13.970	5.470	5.150	38.770	3.780	11.03	16.650	3.350	5.760	5.140	32.780	7.800	5.95	60.09	24.86	338.00	39.760	682.00	2208	349	27.24	174.00	11.700	30.960	38.010	10.120	76.070	213.000	35.85	86.63	43.82	185.00	2088.00
146	28	5	25	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	27.69	16.65	26.730	24.380	9.95	7.920	2.820	4.000	58.110	1.790	8.23	7.760	1.750	3.200	3.390	44.050	4.860	3.77	51.92	25.90	506.00	38.490	451.00	1325	306	19.69	141.00	6.280	18.330	27.170	7.470	68.020	184.000	35.13	89.68	25.77	160.00	2691.00
147	28	8	25	younger	Female	Low	3	No	Yes	BTT	HMII	Alive s/p OHT	alive	87840	57990	66.02	95.60	0.11	15.80	89.00	9.49	1.56	5.02	45.89	7.18	43.54	3.39	6.10	19.51	56.71	84.23	3.14	9.95	10.70	72.13	52.73	7.06	17.53	0.74	3.13	5.13	0.13	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
148	29	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	28.88	11.37	38.621	18.644	2.81	9.882	3.700	4.484	10.147	2.017	4.37	16.099	1.929	2.217	2.373	22.448	5.366	2.67	56.84	19.09	121.00	165.000	278.00	2642	893	12.58	118.00	9.063	71.540	36.892	9.225	98.841	153.000	9.87	43.25	1.92	91.04	849.00
149	29	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	32.32	11.74	23.505	14.109	2.81	8.767	2.565	4.264	25.300	2.483	4.11	23.064	1.929	2.217	2.373	38.737	17.441	3.48	47.53	43.53	86.72	138.000	254.00	4090	934	12.58	156.00	9.063	38.049	32.360	7.475	132.000	173.000	9.37	53.25	1.92	97.03	1026.00
150	29	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	25.42	7.75	19.917	18.280	2.72	2.813	1.666	2.416	11.420	1.760	3.33	3.635	1.630	1.230	1.693	23.780	15.880	17.02	38.18	30.19	301.00	163.000	98.45	422	469	7.34	128.00	14.917	59.891	23.381	4.874	81.038	142.000	5.65	29.22	1.92	55.92	693.00
151	29	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	16.71	7.06	19.917	15.917	2.72	2.813	1.666	2.229	11.848	12.332	3.08	1.100	1.350	1.230	2.025	15.579	10.484	3.34	28.88	28.36	209.00	154.000	88.98	365	499	7.34	107.00	36.898	76.513	22.115	5.734	83.628	132.000	5.65	26.13	1.92	61.63	668.00
152	29	8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	40.77	11.37	32.156	21.931	2.81	11.022	2.105	3.003	6.884	14.236	3.08	1.969	2.247	3.356	2.828	13.545	9.354	3.89	55.29	20.23	152.00	235.000	81.91	458	893	17.01	128.00	38.439	108.000	32.360	9.225	88.759	142.000	7.31	140.00	1.92	79.15	1019.00
153	30	0	49	younger	Male	Low	3	No	NA	DT	HMII	Alive	alive	196315	122053	62.17	95.37	60.66	22.66	91.10	8.90	0.45	4.95	85.57	6.30	5.42	2.71	13.13	28.32	20.05	86.32	2.45	5.42	0.96	98.68	94.19	2.50	2.96	4.63	1.24	0.33	0.89	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
154	30	1	49	younger	Male	Low	3	No	NA	DT	HMII	Alive	alive	91909	37622	40.93	96.10	43.22	38.85	95.48	4.52	0.14	2.19	85.07	3.09	10.33	1.51	21.03	27.23	9.08	88.84	1.13	3.91	0.23	98.77	88.27	1.16	1.16	2.34	0.38	0.21	0.45	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
155	30	5	49	younger	Male	Low	3	No	NA	DT	HMII	Alive	alive	153762	86281	56.11	95.83	65.54	24.24	91.12	8.88	0.65	2.84	81.43	6.30	9.62	2.65	21.14	28.73	15.89	83.00	1.97	6.74	0.36	98.22	93.16	3.20	2.29	5.47	1.11	0.77	1.56	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
156	30	8	49	younger	Male	Low	3	No	NA	DT	HMII	Alive	alive	82406	47485	57.62	92.71	65.75	17.46	91.79	8.21	1.08	1.99	76.35	5.70	15.41	2.54	20.56	26.95	11.99	78.67	1.35	7.97	0.28	97.55	85.62	2.65	2.56	5.19	1.18	0.86	1.79	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
157	31	0	62	older	Male	High	2	No	NA	BTT	CMAG	Died	dead	105070	38521	36.66	98.39	47.84	12.88	60.82	39.18	11.08	2.89	33.87	29.43	26.68	10.01	19.72	21.92	2.99	59.35	14.64	20.23	3.45	81.40	46.10	24.47	24.27	24.04	11.00	17.02	1.51	8.54	60.57	363.000	41.314	133.00	3.271	11.758	2.224	42.519	17.512	99.72	208.000	1.950	1.450	2.382	76.080	15.538	10.70	41.79	124.00	480.00	240.000	583.00	1205	2213	43.09	202.00	15.656	88.621	44.444	4.619	159.000	99.662	8.24	246.00	0.39	1505.00	1842.00
158	31	1	62	older	Male	High	2	No	NA	BTT	CMAG	Died	dead	162336	73080	45.02	99.08	49.35	13.95	74.24	25.76	7.77	3.84	45.21	19.33	28.85	6.61	19.16	23.45	0.81	65.34	14.40	13.50	3.17	86.68	71.39	23.26	17.70	15.21	6.93	13.29	0.88	17.15	153.00	437.000	50.974	202.00	14.614	15.550	2.802	77.669	27.576	127.00	346.000	2.504	2.393	2.558	388.000	22.806	20.90	59.99	150.00	433.00	182.000	771.00	1505	2390	54.32	262.00	13.926	102.000	68.086	7.153	313.000	169.000	9.39	373.00	0.39	2102.00	1842.00
159	31	3	62	older	Male	High	2	No	NA	BTT	CMAG	Died	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	21.40	85.72	386.000	52.666	141.00	13.237	8.380	3.206	207.000	23.994	108.00	256.000	3.104	4.254	3.299	844.000	34.327	22.27	70.68	205.00	540.00	278.000	454.00	1971	4894	52.04	226.00	9.089	95.130	74.895	9.657	206.000	230.000	12.58	318.00	19.30	1420.00	1900.00
160	31	5	62	older	Male	High	2	No	NA	BTT	CMAG	Died	dead	111264	60487	54.36	99.15	45.94	16.13	73.16	26.84	4.64	1.82	52.04	20.07	20.94	6.95	22.93	23.47	0.50	77.11	9.30	9.58	1.20	88.92	81.82	22.20	19.01	16.98	6.89	13.57	1.36	6.39	80.87	363.000	54.635	155.00	11.208	6.816	1.861	71.052	30.424	100.00	233.000	1.920	1.450	2.382	42.999	22.377	14.33	34.41	116.00	363.00	200.000	364.00	1276	1849	38.67	198.00	7.295	92.379	47.259	9.966	195.000	136.000	2.89	326.00	19.30	1292.00	1072.00
161	31	8	62	older	Male	High	2	No	NA	BTT	CMAG	Died	dead	165870	67935	40.96	98.88	43.01	15.86	70.23	29.77	7.85	1.88	40.39	20.49	29.66	9.46	22.70	29.00	1.88	68.61	9.60	16.11	1.47	89.57	69.00	20.12	16.59	17.10	7.02	12.40	2.06	8.54	55.67	299.000	54.916	168.00	10.543	4.146	2.224	51.707	60.924	80.71	211.000	2.314	2.393	3.299	24.277	15.538	9.59	56.39	99.84	439.00	210.000	312.00	968	1056	55.43	187.00	9.350	73.454	31.722	12.711	151.000	109.000	5.73	251.00	0.39	1247.00	1465.00
162	32	0	72	older	Male	Low	4	Yes	NA	BTT	HMII	Died	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.77	2.98	33.241	14.342	3.42	2.970	0.420	0.853	3.452	2.290	13.82	3.190	0.940	0.850	2.360	50.340	2.770	5.07	17.40	64.31	653.00	26.894	241.00	316	496	12.21	15.96	3.050	12.001	8.034	2.282	17.749	119.000	3.85	236.00	0.92	44.32	1793.00
163	32	1	72	older	Male	Low	4	Yes	NA	BTT	HMII	Died	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.77	24.67	100.000	10.761	20.24	2.970	8.281	0.853	136.000	3.964	19.25	3.688	0.940	1.090	2.360	130.000	11.629	6.13	45.48	139.00	541.00	88.075	437.00	264	768	20.97	77.56	5.231	40.568	66.481	1.717	408.000	245.000	55.44	141.00	16.95	74.91	10443.75
164	32	3	72	older	Male	Low	4	Yes	NA	BTT	HMII	Died	dead	424375	21971	5.18	71.78	56.98	18.53	79.84	20.16	3.08	0.51	57.91	14.07	24.06	3.97	31.74	15.95	13.65	57.94	0.65	28.51	0.17	93.57	53.33	2.87	9.69	15.61	3.87	2.16	3.99	2.18	6.65	125.000	14.770	4.61	2.320	14.644	1.590	22.499	1.520	18.98	2.120	1.920	1.450	1.980	72.656	11.749	1.99	23.32	48.13	195.00	286.000	323.00	579	874	10.05	63.62	2.448	45.479	16.356	0.944	52.863	60.307	4.34	61.08	0.39	52.69	813.00
165	32	5	72	older	Male	Low	4	Yes	NA	BTT	HMII	Died	dead	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.77	10.24	45.698	19.436	2.80	2.970	13.723	0.458	31.010	2.290	9.91	3.190	0.940	0.850	2.360	109.000	9.904	3.42	31.87	57.12	177.00	35.314	337.00	361	834	30.99	61.08	3.916	39.476	11.877	2.878	32.504	142.000	2.92	154.00	0.92	69.60	1160.00
166	32	8	72	older	Male	Low	4	Yes	NA	BTT	HMII	Died	dead	177249	103442	58.36	76.40	15.43	5.64	65.13	34.87	5.21	0.20	35.56	27.91	31.90	4.62	26.60	8.28	13.24	67.89	0.29	20.13	0.00	78.60	22.22	5.50	16.09	25.56	6.80	4.73	0.89	2.18	2.98	74.914	45.496	2.31	2.320	4.146	1.590	63.920	1.520	21.62	2.120	20.861	1.450	1.980	773.000	19.378	33.60	45.46	368.00	630.00	220.000	265.00	523	1437	110.00	102.00	4.899	63.342	35.967	3.045	101.000	80.459	15.43	193.00	0.39	234.00	2752.00

Identification of clusters

We double standardized the data and biclustered it. We found 3 clusters of patients, and 3 clusters of B-cells.

require(pheatmap, quietly = T)
require(RColorBrewer, quietly = T)


colorfun <- function(nlevels, ...){
    if(nlevels > 10) return(standardColors(nlevels))
    if(nlevels <=10) return(pal_d3(...)(nlevels))
} 

annotation.row <- data.frame("Molecule" = factor(c("none", rep("B-cell",29), rep("cytokine",38))))
annotation.col <- df.raw[,c(1,4,5,6,7,8,9,10,11,12,13)]
annotation.colors <- lapply(colnames(annotation.col), function(nam){
    colrs <- colorfun(nlevels(annotation.col[[nam]]))
    names(colrs) <- levels(annotation.col[[nam]])
    return(colrs)
})
names(annotation.colors) <- colnames(annotation.col)
annotation.colors[["Molecule"]] <- colorfun(nlevels(annotation.row$Molecule))
names(annotation.colors[["Molecule"]]) <- levels(annotation.row$Molecule)

df.patient <- double_standardize(t(na.omit(df.raw[,c(3,bcellcyto)])))
rownames(annotation.row) <- rownames(df.patient)
pheatmap(df.patient, 
         color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
         breaks = c(seq(-max(abs(df.patient), na.rm=T), 0, length.out = 50), seq(0.001, max(abs(df.patient), na.rm=T), length.out = 50)),
         #scale = "row",
         fontsize = 7,
         cutree_rows = 4,
         cutree_cols = 4,
         cluster_cols = T,
         cluster_rows = T,
         annotation_col = annotation.col,
         annotation_row = annotation.row,
         annotation_colors = annotation.colors,
         clustering_method = "ward.D2"
)

Metadata associations

We computed the statistical associations between sample groups specified by the metadata.

Survival

We computed metadata factors that were statistically associated with survival.

df.factors <- df[match(levels(df$PatientID), df$PatientID),]
isfactor <- which(sapply(df.factors, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Survival","Outcome", "LowIntermacs"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$Survival)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)

	OR	lowerCL	upperCL	pvalue	qvalue	Factor
1	4.900	0.7440	39.00	0.0974	0.681	RVAD
2	NA	NA	NA	0.354	0.823	Device Type
3	2.610	0.1070	188.00	0.569	0.823	Sensitized
4	NA	NA	NA	0.616	0.823	InterMACS
5	0.677	0.0853	5.94	0.674	0.823	Sex
6	1.480	0.2450	9.82	0.705	0.823	AgeGreater60
7	0.879	0.0656	7.89	1	1	VAD Indication

InterMACS

We computed metadata factors that were statistically associated with a binarized InterMACS score.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("LowIntermacs", "InterMACS"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$LowIntermacs)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)

	OR	lowerCL	upperCL	pvalue	qvalue	Factor
1	NA	NA	NA	0.135	0.983	Device Type
2	3.070	0.520	21.10	0.246	0.983	AgeGreater60
3	0.583	0.062	5.43	0.653	1	VAD Indication
4	NA	NA	NA	0.689	1	Outcome
5	1.830	0.288	14.60	0.689	1	RVAD
6	1.310	0.116	15.90	1	1	Sensitized
7	1.210	0.140	9.40	1	1	Sex
8	0.956	0.153	6.40	1	1	Survival

Sensitization

We computed metadata factors that were statistically associated with sensitization.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Sensitized"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$Sensitized)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)

	OR	lowerCL	upperCL	pvalue	qvalue	Factor
1	0.127	0.00192	1.99	0.119	1	Sex
2	2.610	0.10700	188.00	0.569	1	Survival
3	NA	NA	NA	0.569	1	Outcome
4	0.426	0.02530	5.03	0.608	1	RVAD
5	NA	NA	NA	0.843	1	Device Type
6	1.310	0.11600	15.90	1	1	LowIntermacs
7	1.230	0.10100	15.40	1	1	AgeGreater60
8	NA	NA	NA	1	1	InterMACS
9	0.000	0.00000	Inf	1	1	VAD Indication

Sex

We computed metadata factors that were statistically associated with sex.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Sex"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$Sex)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)

	OR	lowerCL	upperCL	pvalue	qvalue	Factor
1	0.127	0.00192	1.99	0.119	0.89	Sensitized
2	3.860	0.48600	49.80	0.198	0.89	AgeGreater60
3	NA	NA	NA	0.368	1	Outcome
4	NA	NA	NA	0.447	1	Device Type
5	0.677	0.08530	5.94	0.674	1	RVAD
6	0.677	0.08530	5.94	0.674	1	Survival
7	1.210	0.14000	9.40	1	1	LowIntermacs
8	NA	NA	NA	1	1	InterMACS
9	1.840	0.15200	103.00	1	1	VAD Indication

Age

We computed metadata factors that were statistically associated with age.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("AgeGreater60"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$AgeGreater60)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)

	OR	lowerCL	upperCL	pvalue	qvalue	Factor
1	0.228	0.0274	1.45	0.114	0.442	RVAD
2	NA	NA	NA	0.128	0.442	InterMACS
3	NA	NA	NA	0.159	0.442	Outcome
4	3.860	0.4860	49.80	0.198	0.442	Sex
5	3.070	0.5200	21.10	0.246	0.442	LowIntermacs
6	NA	NA	NA	0.35	0.525	Device Type
7	1.950	0.2230	25.90	0.655	0.794	VAD Indication
8	1.480	0.2450	9.82	0.705	0.794	Survival
9	1.230	0.1010	15.40	1	1	Sensitized

VAD Indication

We computed metadata factors that were statistically associated with VAD Indication.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("VAD Indication"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$`VAD Indication`)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)

	OR	lowerCL	upperCL	pvalue	qvalue	Factor
1	NA	NA	NA	0.000242	0.00217	Outcome
2	0.000	0.0000	1.34	0.0619	0.278	RVAD
3	0.583	0.0620	5.43	0.653	1	LowIntermacs
4	1.950	0.2230	25.90	0.655	1	AgeGreater60
5	NA	NA	NA	0.837	1	InterMACS
6	NA	NA	NA	0.877	1	Device Type
7	1.840	0.1520	103.00	1	1	Sex
8	0.000	0.0000	Inf	1	1	Sensitized
9	0.879	0.0656	7.89	1	1	Survival

RVAD

We computed metadata factors that were statistically associated with RVAD.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("RVAD"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$RVAD)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)

	OR	lowerCL	upperCL	pvalue	qvalue	Factor
1	NA	NA	NA	0.00868	0.0782	Device Type
2	0.000	0.0000	1.34	0.0619	0.205	VAD Indication
3	NA	NA	NA	0.0739	0.205	Outcome
4	4.900	0.7440	39.00	0.0974	0.205	Survival
5	0.228	0.0274	1.45	0.114	0.205	AgeGreater60
6	NA	NA	NA	0.327	0.49	InterMACS
7	0.426	0.0253	5.03	0.608	0.689	Sensitized
8	0.677	0.0853	5.94	0.674	0.689	Sex
9	1.830	0.2880	14.60	0.689	0.689	LowIntermacs

Device-type

We computed metadata factors that were statistically associated with Device Type.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Device Type"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$`Device Type`)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = T, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = T, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)

	OR	lowerCL	upperCL	pvalue	qvalue	Factor
1	NA	NA	NA	0.0089	0.0801	RVAD
2	NA	NA	NA	0.139	0.531	LowIntermacs
3	NA	NA	NA	0.274	0.531	InterMACS
4	NA	NA	NA	0.346	0.531	Outcome
5	NA	NA	NA	0.352	0.531	AgeGreater60
6	NA	NA	NA	0.354	0.531	Survival
7	NA	NA	NA	0.443	0.569	Sex
8	NA	NA	NA	0.835	0.874	Sensitized
9	NA	NA	NA	0.874	0.874	VAD Indication

Visualization of metadata associations

We used the GGally package to visualize pairwise dependencies between sample groups, separating out survivors and non-survivors (indicated by color).

df.temp <- df.factors
colnames(df.temp) <- make.names(colnames(df), unique = T)
#invisible(
suppressWarnings(
    suppressMessages(
        suppressWarnings(ggpairs(df.temp, 
                #mapping = aes(color = Survival), 
                columns = colnames(df.temp)[c(3,5,6,7,8,9,10,11,12,13)]) +
            ggplot2::theme_grey(base_size = 7)
    )))

#)

Variability of MCS devices

PCA

Using Principal Component Analysis (PCA), we saw large variability between device types, compared to the variability within device types. We also saw large variability between individual patients. None of the other features were clearly separable.

suppressMessages(require(ggbiplot, quietly = T))
require(ggsci, quietly = T)

isna <- unique(unlist(apply(df[,c(bcellcyto)], 2, function(x) which(is.na(x)))))
pca <- prcomp(double_standardize(df[-isna, c(bcellcyto)]), center = TRUE, scale. = TRUE)

colorfun <- function(grouping, ...){
    if(nlevels(factor(grouping)) > 10) scale_color_discrete(...)
    if(nlevels(factor(grouping)) <=10) scale_color_d3(...)
} 

plotvars <- names(df)[c(11,1:13)]
plots.pca <- mclapply(plotvars, function(this_var){
    this_groups <- df[-isna, this_var]
    if(is.numeric(this_groups)) this_groups <- cut(this_groups, 4)
    
    ggbiplot(pca,
             groups = this_groups,
             ellipse = TRUE,
             alpha = 0.3,
             varname.size = 1.2) + 
        colorfun(this_groups, name = this_var) +
        ggtitle(paste0(this_var, " variability")) +
        theme_classic() 
}, mc.cores = detectCores()-1)
names(plots.pca) <- plotvars
#plots.pca$PatientID
#plots.pca$`Device Type`

for(ii in 1:length(plots.pca)){
    cat("  \n###", names(plots.pca)[ii], "\n")
    suppressWarnings(print(plots.pca[[ii]]))
    cat("  \n")
}

Device Type

PatientID

Time

Age

AgeGreater60

Sex

LowIntermacs

InterMACS

RVAD

Sensitized

VAD Indication

Device Type

Outcome

Survival

Fisher’s exact test

We analyzed the dependency of device types on the other discrete variables using Fisher’s exact test.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[names(isfactor) != "Device Type"], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$`Device Type`)
                      })

fisher.p <- sapply(contingency, function(this_table){
    fisher.test(this_table, simulate.p.value = T, B = 10000)$p
})
fisher.q <- p.adjust(fisher.p, method = "BH")

signif.ix <- which(fisher.p < 0.05)
signif.order <- sort(fisher.q[signif.ix], index.return = T)$ix
for(this_ix in signif.ix[signif.order]){
    cat("  \n###", names(contingency)[this_ix], "\n")
    cat(paste0("Benjamini-Hochberg qvalue = ", signif(fisher.q[this_ix], 2)),
        ". \n")
    print(kable(contingency[this_ix][[1]],  row.names = T) %>%
              kable_styling(bootstrap_options = c("striped", 
                                                  "hover", 
                                                  "condensed",
                                                  "responsive"),
                            font_size = 12)
    )
    cat("  \n")
}

RVAD

Benjamini-Hochberg qvalue = 0.077 .

	HMII	CMAG	HVAD	PVAD	TAH
No	15	1	2	0	0
Yes	5	0	0	3	2

One-way repeated measures anova

We first analyzed the differences in B-cell levels across device types using a one-way repeated measures anova. Here we report any variables that had a statistically significant variance (\(p<0.05\)) across devices, time, or their interaction.

suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))

df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)

varnames <- colnames(df.device)[c(bcellcyto)]
models.device <- mclapply(varnames, function(this_var){
    this_formula <- as.formula(paste0(this_var, " ~ Device.Type + (1|PatientID)"))
    invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
    this_anova <- Anova(this_model, type = 2)
    pvals <- this_anova$`Pr(>Chisq)`[c(1)]
    return(list(model = this_model,
                pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[c(bcellcyto)]

pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- colnames(df)[c(bcellcyto)]
colnames(pvals) <- c("pvalue")

qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[c(bcellcyto)]
colnames(qBH) <- c("qvalue")

sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)

sig.qtable <- cbind(pvals,qBH)[sigvars,,drop=F][order(apply(pvals[sigvars,,drop=F], 1, min)),,drop=F]

qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
    mutate(
        `B-cell` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>%
    kable( escape = F,
           digits = 20,
           row.names = T,
           caption = "Significant device-type q-values") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%")

Significant device-type q-values

	pvalue	qvalue	B-cell
1	1.2e-23	8e-22	IL-4
2	1.7e-16	5.8e-15	IL-1RA
3	0.00013	0.003	MCP-1
4	0.011	0.18	CD27-IgD+ mature naive
5	0.023	0.27	MIP-1a
6	0.026	0.27	IL-6
7	0.029	0.27	IL-8
8	0.045	0.35	CD19+CD5+

We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the fit. Note that the reference level for the devices is the HeartMate-II (HMII).

require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))

invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
    stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}

plots.ts <- mclapply(rownames(qtable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            scale_color_d3() + scale_fill_d3() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)

for(ii in c(1:length(plots.ts))){
    for(jj in 1:length(plots.ts[[ii]])){
        sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
        sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
        if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
        sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
        cat("  \n###", rownames(qtable)[ii], "\n")
        print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
                  kable_styling(bootstrap_options = c("striped", 
                                                      "hover", 
                                                      "condensed",
                                                      "responsive"),
                                font_size = 12)
        )
        cat("  \n")
        suppressWarnings(print(plots.ts[[ii]][[jj]]))
        cat("  \n")
    }
}

IL-4

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH	220.662	21.04556	96.0249	10.48497	0

IL-1RA

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	1331.313	152.1179	15.06184	8.751848	3e-07

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	1865.109	407.1979	16.04335	4.580351	0.000306

CD27-IgD+ mature naive

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypePVAD	-33.85486	10.06687	23.54822	-3.362998	0.0026287

MIP-1a

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeHVAD	32.60928	12.418797	15.28209	2.625801	0.0188723
Device.TypePVAD	16.86374	7.943291	17.68110	2.123017	0.0481402

IL-6

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	189.3771	68.51606	12.66841	2.763981	0.0164291

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	77.25016	33.74592	15.23866	2.289171	0.0367422
Device.TypeTAH	73.38816	33.74592	15.23866	2.174727	0.0457932

CD19+CD5+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypePVAD	23.16366	8.615765	23.18582	2.688521	0.0130627

Linear mixed-effect model

We next analyzed the differences in B-cell levels across device types using a linear mixed effect model with time as a continuous variable, and included the interaction term. Here we report variables that had a statistically significant variance (Benjamini-Hochberg \(p<0.05\)) across devices, time, or their interaction.

suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))

df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)

varnames <- colnames(df.device)[bcellcyto]
models.device <- mclapply(varnames, function(this_var){
    this_formula <- as.formula(paste0(this_var, " ~ Device.Type * Time + (1|PatientID)"))
    invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
    this_anova <- Anova(this_model, type = 2)
    pvals <- this_anova$`Pr(>Chisq)`[c(1,2,3)]
    return(list(model = this_model,
                pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[bcellcyto]

pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- varnames
colnames(pvals) <- c("Device", "Time", "Device:Time")

qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[bcellcyto]
colnames(qBH) <- colnames(pvals)

sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)
sig.qtable <- qBH[sigvars,][order(apply(qBH[sigvars,], 1, min)),]

qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
    mutate(
        `B-cell` = row.names(.),
        Device = cell_spec(Device, color = ifelse(qtable$Device > 0.05, "grey", "red")),
        `Device:Time` = cell_spec(`Device:Time`, color = ifelse(qtable$`Device:Time` > 0.05, "grey", "red")),
        `Time` = cell_spec(`Time`, color = ifelse(qtable$`Time` > 0.05, "grey", "red"))
    ) %>%
    kable( escape = F,
           digits = 20,
           row.names = T,
           caption = "Significant device-type q-values") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%")

Significant device-type q-values

	Device	Time	Device:Time	B-cell
1	8.9e-76	0.0033	1e-40	IL-4
2	1.1e-14	1	0.22	IL-1RA
3	0.78	0.0013	0.0041	CD19+CD11b+
4	0.22	0.98	0.0023	MIP-1a
5	0.0033	1	0.57	MCP-1
6	0.98	0.019	1	CD19+CD5+CD11b+
7	0.64	0.023	1	CD268 of +27-38++transitional
8	0.48	0.089	1	CD19+CD268+
9	1	0.089	0.78	IL-1b
10	1	0.095	0.98	CD19+27-38+CD5+transitionals
11	1	0.12	1	CD19 of live lymph
12	0.57	0.31	0.12	CD27+38++plasma blasts
13	0.19	0.24	0.3	CD27-IgD+ mature naive
14	0.57	0.22	1	CD27+IgD- switched memory
15	0.22	1	0.97	IL-6
16	0.34	0.22	1	CD19+CD5+
17	1	0.22	1	CD19+CD5+CD24hi
18	1	0.22	1	IL-3
19	1	0.22	0.76	sCD40L
20	0.98	0.23	1	num lymph
21	0.23	1	1	IL-8

We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit. Note that the reference level for the devices is the HeartMate-II (HMII).

require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))

invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
    stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}

plots.ts <- mclapply(rownames(qtable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            scale_color_d3() + scale_fill_d3() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)

for(ii in c(1:length(plots.ts))){
    for(jj in 1:length(plots.ts[[ii]])){
        sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
        sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
        if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
        sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
        cat("  \n###", rownames(qtable)[ii], "\n")
        print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
                  kable_styling(bootstrap_options = c("striped", 
                                                      "hover", 
                                                      "condensed",
                                                      "responsive"),
                                font_size = 12)
        )
        cat("  \n")
        suppressWarnings(print(plots.ts[[ii]][[jj]]))
        cat("  \n")
    }
}

IL-4

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:Time	57.66099	4.0869	90.99768	14.10873	0

IL-1RA

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	1583.30519	170.53857	23.97074	9.284147	0.0000000
Device.TypeCMAG:Time	-74.12118	22.72778	71.75398	-3.261260	0.0016987

CD19+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:Time	2.1101624	0.5538816	89.91926	3.809771	0.0002541
Device.TypePVAD:Time	0.8054722	0.2943289	88.29842	2.736640	0.0075041
Time	0.2628129	0.1188074	89.80589	2.212092	0.0294973

MIP-1a

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeHVAD:Time	15.08983	3.068267	74.37332	4.918031	5.1e-06

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	2455.3824	490.26835	32.93458	5.008242	0.0000181
Device.TypeCMAG:Time	-173.6232	81.38512	73.35568	-2.133353	0.0362406

CD19+CD5+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.290629	0.1150892	91.27483	2.525249	0.0132869

CD268 of +27-38++transitional

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	-0.7873216	0.3030382	89.79933	-2.598094	0.0109556

CD19+CD268+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	-0.6425061	0.230173	88.33548	-2.791405	0.0064310
Device.TypePVAD	-37.7748686	16.138544	27.53030	-2.340662	0.0267323

IL-1b

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.3273811	0.1608446	72.42554	2.035387	0.0454716

CD19+27-38+CD5+transitionals

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.0910247	0.0395736	89.94149	2.30014	0.0237538

CD19 of live lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	-0.3683497	0.140373	93.29085	-2.624078	0.0101509

CD27+38++plasma blasts

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypePVAD:Time	0.2768179	0.1033923	88.20166	2.677355	0.0088487

CD27-IgD+ mature naive

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypePVAD	-32.958219	10.7368535	29.72853	-3.069635	0.0045463
Device.TypeTAH:Time	-2.247904	0.8166692	89.34041	-2.752527	0.0071618

CD27+IgD- switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.3654018	0.1606941	88.62458	2.273897	0.0253875
Device.TypePVAD	25.0335542	10.8348873	28.15496	2.310458	0.0284029

IL-6

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	304.8154	95.59353	46.0739	3.188661	0.0025701

CD19+CD5+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypePVAD	19.97858	9.433345	33.29569	2.117868	0.0417364

CD19+CD5+CD24hi

	Estimate	Std. Error	df	t value	Pr(>|t|)

IL-3

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.2062222	0.0939434	74.80342	2.195175	0.0312533

sCD40L

	Estimate	Std. Error	df	t value	Pr(>|t|)

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	96.94986	42.04762	34.74735	2.305716	0.0272161

Two-way repeated measures anova

Finally, we attempted to analyze the differences in B-cell levels across device types using a two-way repeated measures ANOVA. Here we report variables that had a statistically significant variance (\(p<0.05\)) across devices, times, or their interaction. As there were 5 device types and 7 timepoints, but only 166 samples, this model is severely underpowered. The posterior belief in any of these results should therefore be quite small (as a consequence of Bayes rule).

suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))

df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)

varnames <- colnames(df.device)[bcellcyto]
models.device <- mclapply(varnames, function(this_var){
    this_formula <- as.formula(paste0(this_var, " ~ Device.Type * factor(Time) + (1|PatientID)"))
    invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
    this_anova <- Anova(this_model, type = 2)
    pvals <- this_anova$`Pr(>Chisq)`[c(1,2,3)]
    return(list(model = this_model,
                pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[bcellcyto]

pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- varnames
colnames(pvals) <- c("Device", "factor(Time)", "Device:factor(Time)")

qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[bcellcyto]
colnames(qBH) <- colnames(pvals)

sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)
sig.qtable <- qBH[sigvars,][order(apply(qBH[sigvars,], 1, min)),]

qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
    mutate(
        `B-cell` = row.names(.),
        Device = cell_spec(Device, color = ifelse(qtable$Device > 0.05, "grey", "red")),
        `Device:factor(Time)` = cell_spec(`Device:factor(Time)`, color = ifelse(qtable$`Device:factor(Time)` > 0.05, "grey", "red")),
        `factor(Time)` = cell_spec(`factor(Time)`, color = ifelse(qtable$`factor(Time)` > 0.05, "grey", "red"))
    ) %>%
    kable( escape = F,
           digits = 20,
           row.names = T,
           caption = "Significant device-type q-values") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%")

Significant device-type q-values

	Device	factor(Time)	Device:factor(Time)	B-cell
1	1.1e-81	3.3e-07	1.4e-123	IL-4
2	1.1e-14	0.55	0.022	IL-1RA
3	0.2	0.09	1.7e-10	CD19+CD5+
4	0.13	0.79	2.5e-09	MIP-1a
5	0.19	0.47	3.5e-06	IL-6
6	0.42	0.046	5e-06	CD27+IgD- switched memory
7	0.66	0.093	7.2e-05	CD19+CD27+
8	0.66	0.38	7.2e-05	CD27-IgD- switched memory
9	0.0017	0.26	2e-04	MCP-1
10	0.69	0.00038	0.0017	CD19+CD11b+
11	0.092	0.19	0.0037	CD27-IgD+ mature naive
12	1	0.0063	1	CD19 of live lymph
13	0.68	0.0063	1	G-CSF
14	0.19	0.012	1	IL-8
15	1	1	0.013	CD19CD24hiCD38-memory
16	1	0.04	1	IL-10
17	0.32	0.046	1	CD27+IgD-IgM+ switched memory
18	1	0.053	1	IL-1b
19	1	1	0.063	CD19+24dim38dim naive mature
20	1	0.66	0.08	CD19+CD27+CD24hi
21	0.66	0.083	1	CD268 of +27-38++transitional
22	0.96	0.14	0.7	CD19+CD5+CD11b+
23	0.37	0.19	0.15	CD19+CD268+
24	1	0.16	1	Eotaxin
25	1	0.19	0.97	MDC
26	0.53	0.26	0.21	CD27+38++plasma blasts
27	0.26	0.21	0.94	IL-15
28	0.23	0.35	1	TNF-b
29	1	0.23	1	IL-5

We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit. Note that the reference level for the time comparisons is timepoint 0, and the reference level for the devices is the HeartMate-II (HMII).

require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))

invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
    stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}

plots.ts <- mclapply(rownames(qtable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            scale_color_d3() + scale_fill_d3() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)

for(ii in c(1:length(plots.ts))){
    for(jj in 1:length(plots.ts[[ii]])){
        sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
        sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
        if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
        sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
        cat("  \n###", rownames(qtable)[ii], "\n")
        print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),], row.names = T) %>%
                  kable_styling(bootstrap_options = c("striped", 
                                                      "hover", 
                                                      "condensed",
                                                      "responsive"),
                                font_size = 12)
        )
        cat("  \n")
        suppressWarnings(print(plots.ts[[ii]][[jj]]))
        cat("  \n")
    }
}

IL-4

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)5	473.44988	23.37471	56.29429	20.254795	0.0000000
Device.TypeTAH:factor(Time)8	398.35765	23.34425	56.24413	17.064486	0.0000000
Device.TypeTAH:factor(Time)3	220.44490	23.34425	56.24413	9.443220	0.0000000
Device.TypeTAH:factor(Time)1	53.48854	23.37471	56.29429	2.288309	0.0258953

IL-1RA

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG	1277.2905	193.4375	37.65943	6.603117	0.0000001
Device.TypeCMAG:factor(Time)1	693.5273	189.4589	57.30055	3.660569	0.0005499
Device.TypePVAD:factor(Time)5	-268.5598	130.7894	58.77891	-2.053375	0.0444945

CD19+CD5+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)5	79.25674	11.074689	67.92417	7.156566	0.0000000
Device.TypePVAD	35.52841	9.894557	43.53672	3.590703	0.0008326
Device.TypePVAD:factor(Time)3	-25.18076	8.751886	71.43189	-2.877181	0.0052872
Device.TypePVAD:factor(Time)5	-31.58267	11.241069	73.06155	-2.809579	0.0063611
Device.TypePVAD:factor(Time)8	-20.60508	7.572762	69.16235	-2.720946	0.0082276

MIP-1a

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeHVAD:factor(Time)8	159.25574	23.02507	59.36219	6.916624	0.0000000
Device.TypePVAD:factor(Time)3	46.08958	15.73936	61.98365	2.928301	0.0047623
Device.TypeHVAD:factor(Time)1	47.74151	23.05589	59.40146	2.070686	0.0427425

IL-6

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG:factor(Time)3	798.6437	139.2084	56.45800	5.737037	0.0000004
Device.TypeTAH:factor(Time)1	310.2112	139.3936	56.50358	2.225434	0.0300595
Device.TypeCMAG:factor(Time)1	291.1312	139.3936	56.50358	2.088556	0.0412650

CD27+IgD- switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypePVAD:factor(Time)3	-44.11347	8.159997	70.32571	-5.406064	0.0000008
Device.TypePVAD	46.55839	10.997051	35.92275	4.233716	0.0001521
Device.TypePVAD:factor(Time)8	-25.13824	7.037695	68.87484	-3.571943	0.0006518
Device.TypePVAD:factor(Time)5	-35.76427	10.505189	71.34328	-3.404439	0.0010914
Device.TypeHVAD:factor(Time)1	22.95446	9.340788	68.84096	2.457443	0.0165160
Device.TypeTAH:factor(Time)1	19.80313	9.340788	68.84096	2.120071	0.0376068

CD19+CD27+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypePVAD:factor(Time)3	-44.86147	9.352169	70.81526	-4.796905	0.0000087
Device.TypePVAD	40.90474	11.677321	38.82944	3.502922	0.0011749
Device.TypePVAD:factor(Time)5	-36.38395	12.029880	72.05224	-3.024465	0.0034501
Device.TypePVAD:factor(Time)8	-22.52547	8.075489	69.06666	-2.789363	0.0068193
Device.TypeHVAD:factor(Time)1	26.28504	10.718455	69.03750	2.452316	0.0167261

CD27-IgD- switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)5	71.68679	13.81377	66.76113	5.189516	0.0000021
Device.TypeTAH:factor(Time)14	34.72518	15.34586	76.65481	2.262837	0.0264767
Device.TypePVAD:factor(Time)3	-22.85502	10.65984	75.69366	-2.144030	0.0352412

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG:factor(Time)3	2642.6492	606.3312	58.57383	4.358425	0.0000536
Device.TypeCMAG	1709.5677	556.6078	48.15330	3.071405	0.0034988
factor(Time)1	363.0153	155.7998	59.46423	2.330012	0.0232187
Device.TypeCMAG:factor(Time)8	-1265.9221	606.3312	58.57383	-2.087839	0.0411707

CD19+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)14	52.95791	10.085405	72.02647	5.250946	0.0000015
Device.TypeTAH:factor(Time)1	21.89638	8.043661	70.05708	2.722190	0.0081767
Device.TypePVAD:factor(Time)14	17.80531	7.197456	72.09567	2.473834	0.0157228
Device.TypeTAH:factor(Time)3	16.76692	8.048562	70.13505	2.083219	0.0408780

CD27-IgD+ mature naive

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)5	-49.338009	12.632153	68.31654	-3.905748	0.0002179
Device.TypePVAD	-40.588774	11.742845	41.95254	-3.456469	0.0012664
Device.TypeTAH:factor(Time)14	-42.518754	14.393799	71.18903	-2.953963	0.0042493
Device.TypeTAH:factor(Time)1	-29.588950	11.470396	69.40469	-2.579593	0.0120122
factor(Time)8	-7.421838	2.996063	68.79574	-2.477197	0.0157031
Device.TypeHVAD:factor(Time)1	-23.895494	11.470396	69.40469	-2.083232	0.0409155

CD19 of live lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)1	8.670607	2.838543	69.69727	3.054598	0.0031913
Device.TypeHVAD:factor(Time)5	18.621453	8.106642	68.64255	2.297061	0.0246707
Device.TypeHVAD:factor(Time)3	16.313101	7.998551	68.60745	2.039507	0.0452529

G-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)1	75.26281	30.59711	60.67210	2.459801	0.0167667
Device.TypeTAH:factor(Time)1	251.33419	119.55029	59.29771	2.102330	0.0397798

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)1	48.21632	15.8183	59.67957	3.048135	0.0034288

CD19CD24hiCD38-memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)5	-22.909686	6.066396	68.15378	-3.776490	0.0003363
Device.TypeHVAD:factor(Time)1	12.942595	5.519995	68.69011	2.344675	0.0219407
factor(Time)1	-3.388861	1.568014	68.34505	-2.161244	0.0341830

IL-10

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)1	162.0198	59.75738	58.42885	2.711294	0.0087864

CD27+IgD-IgM+ switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)5	10.711338	3.308708	71.05852	3.237317	0.0018346
factor(Time)3	8.467757	3.003200	71.42371	2.819578	0.0062189
factor(Time)14	8.273763	3.705650	70.67032	2.232742	0.0287354

IL-1b

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+24dim38dim naive mature

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypePVAD:factor(Time)3	-51.79949	13.95422	76.12811	-3.712101	0.0003892

CD19+CD27+CD24hi

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)5	-22.54139	9.215036	68.32804	-2.446154	0.0170182
factor(Time)8	4.88642	2.184243	68.95940	2.237123	0.0285135
Device.TypeHVAD:factor(Time)1	18.31135	8.355636	69.79004	2.191497	0.0317555

CD268 of +27-38++transitional

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)8	-13.21005	5.828370	69.05531	-2.266508	0.0265563
factor(Time)14	-16.08481	7.874549	69.65371	-2.042632	0.0448754

CD19+CD5+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+CD268+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)5	-66.88010	17.16044	67.82056	-3.897342	0.0002253
Device.TypePVAD	-39.35919	17.41639	37.46863	-2.259893	0.0297274

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)

MDC

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)1	-116.6333	46.70457	59.46922	-2.497257	0.0153028

CD27+38++plasma blasts

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeTAH:factor(Time)1	7.049114	2.979632	70.86460	2.365767	0.0207319
Device.TypeCMAG	7.332777	3.630995	44.46773	2.019495	0.0494876

IL-15

	Estimate	Std. Error	df	t value	Pr(>|t|)
Device.TypeCMAG:factor(Time)3	15.986654	6.525592	58.14190	2.44984	0.0173241
factor(Time)8	3.382095	1.641873	58.49596	2.05990	0.0438665

TNF-b

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)1	-48.00227	17.12941	58.05021	-2.802330	0.0068843
factor(Time)3	-45.91700	16.76077	57.61190	-2.739553	0.0081753
factor(Time)5	-34.81589	17.12941	58.05021	-2.032521	0.0466840

IL-5

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)1	-8.197292	2.673786	59.39955	-3.065800	0.0032645
factor(Time)3	-6.821442	2.618986	58.78438	-2.604612	0.0116301
factor(Time)8	-6.460310	2.618986	58.78438	-2.466721	0.0165701
factor(Time)5	-6.183556	2.673786	59.39955	-2.312659	0.0242238

HeartMate-II analysis

The HeartMate-II (HMII) recipients were the largest group, and we analyzed them by themselves due to the previously observed variability across devices.

require(reshape2, quietly = T)
df.HMII <- subset(df, df$`Device Type`=="HMII")
df.long <- melt(df.HMII, id.vars = colnames(df)[1:13])

groups <- make.names(c("AgeGreater60", 
                       "Sex",
                       "LowIntermacs",
                       "RVAD", 
                       "Sensitized",
                       "VAD Indication", 
                       #"Device Type", 
                       "Survival",
                       "Outcome"))

names(df.long) <- make.names(names(df.long))

plots.ts <- mclapply(unique(df.long$variable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            scale_color_aaas() + scale_fill_aaas() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- unique(df.long$variable)

# for(ii in c(1:length(plots.ts))){
#     for(jj in 1:length(plots.ts[[ii]])){
#         suppressWarnings(print(plots.ts[[ii]][[jj]]))
#     }
# } 

PCA

Using PCA, we found large variability between individual patients, compared to the variability within individual patients. None of the other features were clearly separable.

suppressMessages(require(ggbiplot, quietly = T))
require(ggsci, quietly = T)

# Efron's double standardization
double_standardize <- function(x, niter = 100) {
    for(i in 1:niter) x <- t(scale(t(scale(x))))
    return(as.data.frame(x))
}

isna <- unique(unlist(apply(df.HMII[,bcellcyto], 2, function(x) which(is.na(x)))))
pca <- prcomp(double_standardize(df.HMII[-isna, bcellcyto]), center = TRUE, scale. = TRUE)

colorfun <- function(grouping, ...){
    if(nlevels(factor(grouping)) > 10) scale_color_discrete(...)
    if(nlevels(factor(grouping)) <=10) scale_color_d3(...)
} 

plots.pca <- mclapply(names(df.HMII)[1:13], function(this_var){
    this_groups <- df.HMII[-isna, this_var]
    if(is.numeric(this_groups)) this_groups <- cut(this_groups, 4)
    
    ggbiplot(pca,
             groups = this_groups,
             ellipse = TRUE,
             alpha = 0.3,
             varname.size = 1.2) + 
        colorfun(this_groups, name = this_var) +
        ggtitle(paste0(this_var, " variability")) +
        theme_classic() 
}, mc.cores = detectCores()-1)

names(plots.pca) <- names(df.HMII)[1:13]
#plots.pca$PatientID
#plots.pca$`Device Type`

for(ii in 1:length(plots.pca)){
    cat("  \n###", names(plots.pca)[ii], "\n")
    suppressWarnings(print(plots.pca[[ii]]))
    cat("  \n")
}

PatientID

Time

Age

AgeGreater60

Sex

LowIntermacs

InterMACS

RVAD

Sensitized

VAD Indication

Device Type

Outcome

Survival

One-way repeated measures anova

We analyzed the differences in B-cell levels for various features using a one-way repeated measures anova. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.

suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)

df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)

groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]

bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]

models.b <- mclapply(groupvars, function(this_groupvar){
    models.bcells <- lapply(bcells, function(this_bcell){
        this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar, 
                                          " + (1|PatientID)"))
        suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
        this_anova <- Anova(this_model, type = 2)
        this_pvalues <- this_anova$`Pr(>Chisq)`
        names(this_pvalues) <- rownames(this_anova)
        #return(this_pvalues)
        return(list(model = this_model,
                    pvals = this_pvalues))
    })
    names(models.bcells) <- colnames(df)[bcellcyto]
    pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
    rownames(pvalues) <- bcells
    #return(pvalues)
    return(list(model = models.bcells,
                pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1)]))



# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix), 
#                        method = "BH"), 
#               nrow = nrow(pvals.matrix), 
#               ncol = ncol(pvals.matrix), 
#               byrow = F) 
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]

# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]

pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]



kable(shortlist, 
      digits = 3,
      row.names = T,
      caption = "Significant results") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%")

Significant results

	B-cell	parameter	qvalue
163	CD19+27+IgD-38++IgG ASC	LowIntermacs	0.208
167	TNF-a	LowIntermacs	0.266
456	MCP-1	Outcome	0.267
70	lymph	Sex	0.270
49	IL-8	AgeGreater60	0.271
46	IL-15	AgeGreater60	0.271
2	num lymph	AgeGreater60	0.299
3	lymph	AgeGreater60	0.367
523	MCP-1	Survival	0.371
416	CD27+IgD+ unswitched memory	Outcome	0.395
396	G-CSF	VAD.Indication	0.483
83	CD27+IgD+IgM+ nonswitched memory	Sex	0.505
33	TNF-a	AgeGreater60	0.506
415	CD27-IgD- switched memory	Outcome	0.507
51	Eotaxin	AgeGreater60	0.521
215	CD27+IgD+ unswitched memory	RVAD	0.525
29	CD19+27+IgD-38++IgG ASC	AgeGreater60	0.527
20	CD19CD24hiCD38-memory	AgeGreater60	0.530
137	lymph	LowIntermacs	0.530
93	CD19+CD27+CD24hi	Sex	0.532
118	Eotaxin	Sex	0.533
418	CD27+IgD+IgM+ nonswitched memory	Outcome	0.536
116	IL-8	Sex	0.536

We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above one-way anova. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.

require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
    this_group <-siggroups[ii]
    this_bcell <- as.character(shortlist$`B-cell`[ii])
    cat("  \n###", as.character(shortlist$`B-cell`[ii]), "\n")
    
    sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
    sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
    if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
    sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
    
    print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
              kable_styling(bootstrap_options = c("striped", 
                                                  "hover", 
                                                  "condensed",
                                                  "responsive"),
                            font_size = 12)
    )
    cat("  \n")
    
    suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
    cat("  \n")
}

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh	2.078718	0.60011	17.90374	3.463894	0.0027878

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh	18.20785	6.096687	14.89374	2.986515	0.0092805

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied	498.6263	225.4061	12.04031	2.212125	0.0470293

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	-18.44662	6.370603	17.92503	-2.895584	0.0096664

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	44.84973	15.53423	14.72595	2.887155	0.0114543

IL-15

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	6.463922	2.256074	13.72298	2.86512	0.0126788

num lymph

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	-15.22801	5.790664	17.53478	-2.629751	0.0172536

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
Survivaldead	466.6129	178.0397	14.27031	2.620836	0.0198971

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT	16.53063	6.610333	15.90681	2.500726	0.0237124

G-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT	-71.81929	31.45987	13.6818	-2.282886	0.0389804

CD27+IgD+IgM+ nonswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	-16.26667	7.449772	17.76457	-2.183512	0.0426655

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	15.47275	7.106515	15.54261	2.177263	0.045244

CD27-IgD- switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19CD24hiCD38-memory

	Estimate	Std. Error	df	t value	Pr(>|t|)

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+CD27+CD24hi

	Estimate	Std. Error	df	t value	Pr(>|t|)

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD27+IgD+IgM+ nonswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)

Linear mixed-effect model

We analyzed the differences in B-cell levels for various features using a linear mixed effect model, with time as a continuous variable. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.

suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)

df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)

groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]

bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]

models.b <- mclapply(groupvars, function(this_groupvar){
    models.bcells <- lapply(bcells, function(this_bcell){
        this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar, 
                                          " * Time + (1|PatientID)"))
        suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
        this_anova <- Anova(this_model, type = 2)
        this_pvalues <- this_anova$`Pr(>Chisq)`
        names(this_pvalues) <- rownames(this_anova)
        #return(this_pvalues)
        return(list(model = this_model,
                    pvals = this_pvalues))
    })
    names(models.bcells) <- colnames(df)[bcellcyto]
    pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
    rownames(pvalues) <- bcells
    #return(pvalues)
    return(list(model = models.bcells,
                pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1,3)]))



# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix), 
#                        method = "BH"), 
#               nrow = nrow(pvals.matrix), 
#               ncol = ncol(pvals.matrix), 
#               byrow = F) 
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]

# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]

pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]



kable(shortlist, 
      digits = 3,
      row.names = T,
      caption = "Significant results") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%")

Significant results

	B-cell	parameter	qvalue
885	CD27+IgD+ unswitched memory	Outcome:Time	0.000
893	CD19+CD268+	Outcome:Time	0.000
297	CD19+27+IgD-38++IgG ASC	LowIntermacs	0.033
790	IP-10	VAD.Indication:Time	0.035
69	num lymph	AgeGreater60:Time	0.038
514	IL-6	RVAD:Time	0.053
2	num lymph	AgeGreater60	0.060
765	CD19+CD5+CD11b+	VAD.Indication:Time	0.069
770	TNF-a	VAD.Indication:Time	0.077
858	MCP-1	Outcome	0.192
301	TNF-a	LowIntermacs	0.193
219	CD19+24dim38dim naive mature	Sex:Time	0.208
49	IL-8	AgeGreater60	0.229
137	lymph	Sex	0.233
46	IL-15	AgeGreater60	0.237
896	CD19+CD5+	Outcome:Time	0.264
383	IFN-a2	LowIntermacs:Time	0.267
899	CD19+CD5+CD11b+	Outcome:Time	0.280
762	CD19+CD5+	VAD.Indication:Time	0.289
345	CD27-38++ transitional	LowIntermacs:Time	0.298
97	IL-12(p40)	AgeGreater60:Time	0.300
761	CD19+CD11b+	VAD.Indication:Time	0.321
992	MCP-1	Survival	0.324
904	TNF-a	Outcome:Time	0.340
3	lymph	AgeGreater60	0.354
818	CD27+IgD+ unswitched memory	Outcome	0.356
764	CD19+CD5+CD24hi	VAD.Indication:Time	0.362
887	CD27+IgD+IgM+ nonswitched memory	Outcome:Time	0.363
1050	IL-6	Survival:Time	0.367
268	sCD40L	Sex:Time	0.369
91	CD19+CD11b+	AgeGreater60:Time	0.384
123	Fractalkine	AgeGreater60:Time	0.394
377	IL-1b	LowIntermacs:Time	0.409
1049	IL-3	Survival:Time	0.415
115	IFN-a2	AgeGreater60:Time	0.416
791	MCP-1	VAD.Indication:Time	0.437
68	num Total PBMC	AgeGreater60:Time	0.441
380	IL-6	LowIntermacs:Time	0.464
731	G-CSF	VAD.Indication	0.465
77	CD27-38++ transitional	AgeGreater60:Time	0.466
1010	CD3 of live lymph	Survival:Time	0.475
150	CD27+IgD+IgM+ nonswitched memory	Sex	0.476
518	IL-8	RVAD:Time	0.476
898	CD19+CD5+CD24hi	Outcome:Time	0.479
252	Eotaxin	Sex:Time	0.480
523	MCP-1	RVAD:Time	0.487
382	TGF-a	LowIntermacs:Time	0.489
206	CD3 of live lymph	Sex:Time	0.492
33	TNF-a	AgeGreater60	0.493
234	TNF-a	Sex:Time	0.493
783	IL-15	VAD.Indication:Time	0.494
211	CD27-38++ transitional	Sex:Time	0.495
817	CD27-IgD- switched memory	Outcome	0.496
416	CD27+IgD+ unswitched memory	RVAD	0.496
794	MIP-1a	VAD.Indication:Time	0.505
51	Eotaxin	AgeGreater60	0.508
924	IP-10	Outcome:Time	0.508
271	lymph	LowIntermacs	0.513
95	CD19+CD5+CD11b+	AgeGreater60:Time	0.522
126	GM-CSF	AgeGreater60:Time	0.527
128	G-CSF	AgeGreater60:Time	0.529
20	CD19CD24hiCD38-memory	AgeGreater60	0.529
29	CD19+27+IgD-38++IgG ASC	AgeGreater60	0.531
895	CD19+CD11b+	Outcome:Time	0.531
746	CD27+38++plasma blasts	VAD.Indication:Time	0.531
185	Eotaxin	Sex	0.532
820	CD27+IgD+IgM+ nonswitched memory	Outcome	0.533
160	CD19+CD27+CD24hi	Sex	0.533
391	Fractalkine	LowIntermacs:Time	0.535
739	num lymph	VAD.Indication:Time	0.536
371	IL-5	LowIntermacs:Time	0.538
90	CD268 of +27-38++transitional	AgeGreater60:Time	0.539
183	IL-8	Sex	0.540
664	G-CSF	Sensitized:Time	0.540
1027	CD19+CD268+	Survival:Time	0.540
516	TGF-a	RVAD:Time	0.545
386	Eotaxin	LowIntermacs:Time	0.545

We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.

require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
    this_group <-siggroups[ii]
    this_bcell <- as.character(shortlist$`B-cell`[ii])
    cat("  \n###", as.character(shortlist$`B-cell`[ii]), "\n")
    
    sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
    sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
    if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
    sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
    
    print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
              kable_styling(bootstrap_options = c("striped", 
                                                  "hover", 
                                                  "condensed",
                                                  "responsive"),
                            font_size = 12)
    )
    cat("  \n")
    
    suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
    cat("  \n")
}

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:Time	-2.667973	0.494540	62.02792	-5.394858	0.0000011
OutcomeDied post OHT	23.798615	6.761184	17.90907	3.519888	0.0024617

CD19+CD268+

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:Time	-6.8213719	1.5139567	62.00943	-4.505659	0.0000299
Time	-1.2473123	0.3522343	62.49855	-3.541144	0.0007592
OutcomeAlive s/p OHT:Time	0.9383259	0.4056434	62.45638	2.313179	0.0240193

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh	3.058696	0.774233	45.35361	3.950614	0.0002697

IP-10

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:Time	-126.82887	33.85589	54.58765	-3.746139	0.0004345
Time	60.64459	17.34161	54.85011	3.497056	0.0009404

num lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	8206.115	1791.316	1287.688	4.581054	0.0000051
AgeGreater60older:Time	-8800.327	2381.169	2274.895	-3.695802	0.0002244

IL-6

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:Time	32.60694	9.175249	54.21954	3.553793	0.0007958

num lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	8206.115	1791.316	1287.688	4.581054	0.0000051
AgeGreater60older:Time	-8800.327	2381.169	2274.895	-3.695802	0.0002244

CD19+CD5+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:Time	0.7535101	0.2208725	66.53183	3.411516	0.0011029

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:Time	-5.629434	1.680142	55.89628	-3.350570	0.0014504
VAD.IndicationDT	23.272245	10.340359	28.83495	2.250622	0.0322084

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh	23.97608	8.347092	41.27354	2.872387	0.0064058

CD19+24dim38dim naive mature

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	-0.8161430	0.2628953	64.83725	-3.104441	0.0028253
SexMale:Time	0.9045098	0.3076620	64.86966	2.939946	0.0045445

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	-17.61531	7.156886	25.50525	-2.46131	0.0209393

IL-15

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	7.790226	2.627262	23.91797	2.96515	0.0067556

CD19+CD5+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.8658199	0.2549408	64.48772	3.396161	0.0011744
OutcomeDied post OHT:Time	-3.3958199	1.1020936	62.63883	-3.081244	0.0030615
OutcomeAlive s/p OHT:Time	-0.6785876	0.2937450	64.32566	-2.310125	0.0241003

IFN-a2

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	7.094916	2.400303	55.17787	2.955842	0.0045797
LowIntermacsHigh:Time	-8.250956	2.967875	54.85053	-2.780089	0.0074308

CD19+CD5+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.9291898	0.2104973	64.10073	4.414260	0.0000397
OutcomeAlive s/p OHT:Time	-0.8534246	0.2425036	63.97151	-3.519225	0.0008029

CD19+CD5+

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:Time	0.7495301	0.2761314	67.17207	2.714397	0.0084299

CD27-38++ transitional

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.2728189	0.0943080	68.33055	2.892849	0.0051174
LowIntermacsHigh:Time	-0.3029055	0.1126997	67.21515	-2.687721	0.0090598

IL-12(p40)

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	4.488736	1.571299	56.15354	2.856703	0.0059916
AgeGreater60older:Time	-4.915360	1.833222	55.84263	-2.681268	0.0096251

CD19+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:Time	0.5911116	0.2251629	65.39615	2.625262	0.0107693

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
Survivaldead	500.4575	220.1017	30.55848	2.273755	0.0301634

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	-5.527423	1.705528	53.73771	-3.240887	0.0020479
OutcomeAlive s/p OHT:Time	6.230423	2.030162	53.98616	3.068929	0.0033572
OutcomeDied:Time	6.757500	2.229274	53.60892	3.031256	0.0037457
OutcomeDied	-32.035464	13.772117	24.37281	-2.326110	0.0286425
OutcomeAlive s/p OHT	-28.339350	12.411581	25.62297	-2.283299	0.0309577

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.7428399	0.3610038	69.66204	2.057706	0.0433625

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:Time	-2.667973	0.494540	62.02792	-5.394858	0.0000011
OutcomeDied post OHT	23.798615	6.761184	17.90907	3.519888	0.0024617

CD19+CD5+CD24hi

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:Time	0.4514841	0.1801168	68.30031	2.506618	0.0145744

CD27+IgD+IgM+ nonswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:Time	-4.960664	1.778346	62.05844	-2.789481	0.0070021
OutcomeDied post OHT	42.763036	16.441772	20.60956	2.600878	0.0168391

IL-6

	Estimate	Std. Error	df	t value	Pr(>|t|)
Survivaldead:Time	20.49333	8.218759	54.28222	2.493482	0.0157287

sCD40L

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	564.2348	192.6758	56.44318	2.928415	0.0049057
SexMale:Time	-561.8438	225.8697	56.13655	-2.487468	0.0158624

CD19+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.5499941	0.1571744	65.94446	3.499260	0.0008412
AgeGreater60older:Time	-0.5051776	0.2059164	65.50984	-2.453314	0.0168239

Fractalkine

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	12.02533	4.519847	54.65411	2.660561	0.0102192
AgeGreater60older:Time	-12.81993	5.273092	54.32486	-2.431197	0.0183777

IL-1b

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.8271702	0.2556659	53.88754	3.235356	0.0020785
LowIntermacsHigh:Time	-0.7446451	0.3108987	53.73054	-2.395137	0.0201302

IL-3

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.3819202	0.1262965	56.47376	3.023997	0.0037486
Survivaldead:Time	-0.5153221	0.2164933	55.43845	-2.380315	0.0207621

IFN-a2

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	7.368396	2.774815	55.86789	2.655454	0.0103010
AgeGreater60older:Time	-7.704730	3.239224	55.41990	-2.378573	0.0208522
AgeGreater60older	44.726614	21.390894	24.55143	2.090918	0.0470524

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT	680.47441	249.43832	27.52168	2.728027	0.0109658
VAD.IndicationDT:Time	-91.32862	39.28639	57.36259	-2.324688	0.0236507

num Total PBMC

IL-6

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh:Time	-18.54127	8.228838	55.84312	-2.253206	0.0281925

G-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD27-38++ transitional

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.1924463	0.0792502	69.27155	2.428340	0.0177687
AgeGreater60older:Time	-0.2343863	0.1043092	67.73029	-2.247034	0.0279003

CD3 of live lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
Survivaldead:Time	-1.829251	0.8242817	66.33046	-2.219206	0.0298978

CD27+IgD+IgM+ nonswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	-20.7260608	7.6448089	21.84154	-2.711129	0.0128054
Time	-0.9006538	0.3895893	66.29166	-2.311803	0.0239044

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:Time	10.23949	4.619034	54.36592	2.216804	0.030839

CD19+CD5+CD24hi

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.5935719	0.1694987	65.72053	3.501927	0.0008357
OutcomeAlive s/p OHT:Time	-0.5739728	0.1953535	65.49771	-2.938124	0.0045549

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	128.54981	47.668530	18.34905	2.696744	0.0145850
Time	11.40686	4.560498	55.36903	2.501231	0.0153620
SexMale:Time	-11.77662	5.342803	55.18872	-2.204202	0.0316985

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:Time	93.61331	42.84101	54.30507	2.185133	0.0332124

TGF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh:Time	-1.1233582	0.5157778	54.51579	-2.177989	0.0337513
Time	0.8378511	0.4175992	54.60020	2.006352	0.0497822

CD3 of live lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale:Time	-1.143545	0.5277063	66.36504	-2.167011	0.0338279

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale:Time	3.767525	1.740926	57.15748	2.164093	0.0346496
Time	-3.158273	1.483377	57.67143	-2.129110	0.0375269

IL-15

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.4821335	0.1913209	54.97323	2.520026	0.0146688
VAD.IndicationDT:Time	-0.8073232	0.3733218	54.80952	-2.162540	0.0349572

CD27-38++ transitional

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.2418057	0.0994538	65.99623	2.431336	0.0177663
SexMale:Time	-0.2512294	0.1163638	66.05082	-2.159000	0.0344873

CD27-IgD- switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes	8.138537	3.426992	21.4197	2.374834	0.0269627

MIP-1a

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:Time	-2.726267	1.282933	55.03723	-2.125027	0.038089

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	118.1149	47.98599	19.32249	2.461445	0.0234005

IP-10

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeAlive s/p OHT:Time	114.5049	42.55788	53.61146	2.690569	0.009491

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+CD5+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.5382593	0.1602283	67.49846	3.359327	0.0012883
AgeGreater60older:Time	-0.4334330	0.2101801	66.78306	-2.062198	0.0430827

GM-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	34.128884	14.361503	20.92304	2.376415	0.0271092
AgeGreater60older:Time	-4.022234	1.970956	54.45762	-2.040753	0.0461351

G-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	114.60139	42.096120	38.21983	2.722374	0.0097107
AgeGreater60older:Time	-16.24051	7.977444	57.78234	-2.035804	0.0463639

CD19CD24hiCD38-memory

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	2.044292	0.8970807	39.13156	2.278828	0.0282137

CD19+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.7895884	0.2140589	63.20206	3.688650	0.0004710
OutcomeAlive s/p OHT:Time	-0.6983342	0.2465523	63.12486	-2.832397	0.0061934

CD27+38++plasma blasts

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	0.1018402	0.0463711	65.98620	2.196200	0.0315970
VAD.IndicationDT:Time	-0.1781890	0.0880343	66.32796	-2.024086	0.0469921

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	128.54981	47.668530	18.34905	2.696744	0.0145850
Time	11.40686	4.560498	55.36903	2.501231	0.0153620
SexMale:Time	-11.77662	5.342803	55.18872	-2.204202	0.0316985

CD27+IgD+IgM+ nonswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:Time	-4.960664	1.778346	62.05844	-2.789481	0.0070021
OutcomeDied post OHT	42.763036	16.441772	20.60956	2.600878	0.0168391

CD19+CD27+CD24hi

	Estimate	Std. Error	df	t value	Pr(>|t|)

Fractalkine

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	9.181153	4.034219	54.31416	2.275819	0.0268244
LowIntermacsHigh:Time	-10.023051	4.986464	54.05128	-2.010052	0.0494276

num lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:Time	5487.098	2740.267	7534.727	2.002396	0.045278

IL-5

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD268 of +27-38++transitional

	Estimate	Std. Error	df	t value	Pr(>|t|)

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)

G-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+CD268+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	-0.5331745	0.2039893	64.53309	-2.613738	0.0111329

TGF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)
Time	9.357596	4.075572	55.21464	2.29602	0.0254948

Two-way repeated measures anova

We analyzed the differences in B-cell levels for various features using a two-way repeated measures ANOVA. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.

suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)

df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)

groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]

bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]

models.b <- mclapply(groupvars, function(this_groupvar){
    models.bcells <- lapply(bcells, function(this_bcell){
        this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar, 
                                          " * factor(Time) + (1|PatientID)"))
        suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
        this_anova <- Anova(this_model, type = 2)
        this_pvalues <- this_anova$`Pr(>Chisq)`
        names(this_pvalues) <- rownames(this_anova)
        #return(this_pvalues)
        return(list(model = this_model,
                    pvals = this_pvalues))
    })
    names(models.bcells) <- colnames(df)[bcellcyto]
    pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
    rownames(pvalues) <- bcells
    #return(pvalues)
    return(list(model = models.bcells,
                pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1,3)]))



# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix), 
#                        method = "BH"), 
#               nrow = nrow(pvals.matrix), 
#               ncol = ncol(pvals.matrix), 
#               byrow = F) 
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]

# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]

pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]



kable(shortlist, 
      digits = 3,
      row.names = T,
      caption = "Significant results") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%")

Significant results

	B-cell	parameter	qvalue
791	MCP-1	VAD.Indication:factor(Time)	0.000
893	CD19+CD268+	Outcome:factor(Time)	0.000
885	CD27+IgD+ unswitched memory	Outcome:factor(Time)	0.003
530	G-CSF	RVAD:factor(Time)	0.022
297	CD19+27+IgD-38++IgG ASC	LowIntermacs	0.026
790	IP-10	VAD.Indication:factor(Time)	0.027
2	num lymph	AgeGreater60	0.030
90	CD268 of +27-38++transitional	AgeGreater60:factor(Time)	0.073
618	CD27+IgD-IgM+ switched memory	Sensitized:factor(Time)	0.088
770	TNF-a	VAD.Indication:factor(Time)	0.091
858	MCP-1	Outcome	0.183
514	IL-6	RVAD:factor(Time)	0.209
137	lymph	Sex	0.227
766	CD19+27+IgD-38++IgG ASC	VAD.Indication:factor(Time)	0.244
301	TNF-a	LowIntermacs	0.253
498	CD19+27+IgD-38++IgG ASC	RVAD:factor(Time)	0.266
46	IL-15	AgeGreater60	0.268
49	IL-8	AgeGreater60	0.295
345	CD27-38++ transitional	LowIntermacs:factor(Time)	0.296
3	lymph	AgeGreater60	0.300
518	IL-8	RVAD:factor(Time)	0.307
883	CD27+IgD- switched memory	Outcome:factor(Time)	0.350
992	MCP-1	Survival	0.416
383	IFN-a2	LowIntermacs:factor(Time)	0.420
765	CD19+CD5+CD11b+	VAD.Indication:factor(Time)	0.424
69	num lymph	AgeGreater60:factor(Time)	0.426
215	CD27+IgD+ unswitched memory	Sex:factor(Time)	0.466
818	CD27+IgD+ unswitched memory	Outcome	0.469
78	CD27-IgD+ mature naive	AgeGreater60:factor(Time)	0.487
879	CD19+CD27+	Outcome:factor(Time)	0.527
128	G-CSF	AgeGreater60:factor(Time)	0.566
878	CD19+CD27-	Outcome:factor(Time)	0.571
271	lymph	LowIntermacs	0.578
371	IL-5	LowIntermacs:factor(Time)	0.585
526	MIP-1a	RVAD:factor(Time)	0.596
817	CD27-IgD- switched memory	Outcome	0.626
900	CD19+27+IgD-38++IgG ASC	Outcome:factor(Time)	0.634
739	num lymph	VAD.Indication:factor(Time)	0.639
97	IL-12(p40)	AgeGreater60:factor(Time)	0.653
886	CD27+IgD-IgM+ switched memory	Outcome:factor(Time)	0.658
150	CD27+IgD+IgM+ nonswitched memory	Sex	0.662
483	CD27+IgD+ unswitched memory	RVAD:factor(Time)	0.667
217	CD27+IgD+IgM+ nonswitched memory	Sex:factor(Time)	0.677
115	IFN-a2	AgeGreater60:factor(Time)	0.682
491	CD19+CD268+	RVAD:factor(Time)	0.688
33	TNF-a	AgeGreater60	0.706
1027	CD19+CD268+	Survival:factor(Time)	0.717
762	CD19+CD5+	VAD.Indication:factor(Time)	0.719
377	IL-1b	LowIntermacs:factor(Time)	0.719
731	G-CSF	VAD.Indication	0.724
51	Eotaxin	AgeGreater60	0.725
820	CD27+IgD+IgM+ nonswitched memory	Outcome	0.730
904	TNF-a	Outcome:factor(Time)	0.733
183	IL-8	Sex	0.743
490	CD19+27-38+CD5+transitionals	RVAD:factor(Time)	0.745
89	CD19+CD268+	AgeGreater60:factor(Time)	0.745
416	CD27+IgD+ unswitched memory	RVAD	0.748
185	Eotaxin	Sex	0.755
1062	MIP-1a	Survival:factor(Time)	0.764
29	CD19+27+IgD-38++IgG ASC	AgeGreater60	0.765
234	TNF-a	Sex:factor(Time)	0.766
20	CD19CD24hiCD38-memory	AgeGreater60	0.769

We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.

require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
    this_group <-siggroups[ii]
    this_bcell <- as.character(shortlist$`B-cell`[ii])
    cat("  \n###", as.character(shortlist$`B-cell`[ii]), "\n")
    
    sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
    sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
    if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
    sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
    
    print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
              kable_styling(bootstrap_options = c("striped", 
                                                  "hover", 
                                                  "condensed",
                                                  "responsive"),
                            font_size = 12)
    )
    cat("  \n")
    
    suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
    cat("  \n")
}

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:factor(Time)1	1396.314	299.8357	49.76712	4.656931	2.42e-05

CD19+CD268+

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:factor(Time)3	-58.61538	14.204316	46.18057	-4.126590	0.0001523
OutcomeDied post OHT:factor(Time)8	-55.76061	13.538518	46.05387	-4.118664	0.0001567
factor(Time)21	-27.32462	8.270658	46.63558	-3.303802	0.0018378
OutcomeAlive s/p OHT:factor(Time)21	27.40774	9.419763	46.53642	2.909600	0.0055342

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:factor(Time)8	-27.59770	4.932075	46.11295	-5.595555	0.0000012
OutcomeDied post OHT	27.31810	7.270274	22.64763	3.757506	0.0010464
OutcomeDied post OHT:factor(Time)1	-13.37738	5.047487	45.94619	-2.650306	0.0109931

G-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:factor(Time)1	274.3137	66.34657	49.26911	4.134557	0.0001379

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh	3.095	1.279721	68.99999	2.418497	0.0182277

IP-10

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:factor(Time)5	-999.2242	294.0807	48.43045	-3.397789	0.0013666
factor(Time)5	463.4742	156.2366	49.13082	2.966490	0.0046413
VAD.IndicationDT:factor(Time)8	-806.2164	291.6175	48.29663	-2.764637	0.0080454
VAD.IndicationDT:factor(Time)3	-723.7126	291.6175	48.29663	-2.481719	0.0166053
factor(Time)8	335.4664	151.5492	48.68098	2.213581	0.0315752

num lymph

CD268 of +27-38++transitional

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older:factor(Time)8	-27.22177	10.33554	54.58102	-2.633803	0.0109632
factor(Time)14	-24.92941	12.43679	55.44562	-2.004489	0.0499111

CD27+IgD-IgM+ switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
SensitizedYes:factor(Time)3	17.965877	5.302290	23.31979	3.388324	0.0024949
factor(Time)5	9.747852	4.547781	23.30374	2.143431	0.0427391

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:factor(Time)3	-52.08619	14.55496	49.73388	-3.578586	0.0007821
VAD.IndicationDT:factor(Time)8	-51.15105	14.55496	49.73388	-3.514337	0.0009497
VAD.IndicationDT:factor(Time)5	-48.24997	14.67386	49.90668	-3.288159	0.0018522
VAD.IndicationDT	39.41501	12.48552	46.96050	3.156857	0.0027848

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)

IL-6

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:factor(Time)8	303.4749	81.51192	47.97526	3.723074	0.0005171

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	-22.22807	9.001586	53.16948	-2.46935	0.0167811

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)5	4.722429	1.330913	60.14684	3.548263	0.0007592
VAD.IndicationDT:factor(Time)5	-6.577445	1.874482	57.09610	-3.508940	0.0008850

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh	29.20377	11.74819	61.39306	2.48581	0.015663

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:factor(Time)5	11.55470	2.912778	59.01745	3.966900	0.0001997
factor(Time)14	-2.17979	1.043488	56.60859	-2.088945	0.0412201

IL-15

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older	6.953102	3.180869	41.10986	2.185913	0.034572

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD27-38++ transitional

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh:factor(Time)21	-11.380113	2.894440	59.65375	-3.931715	0.0002222
factor(Time)21	9.440964	2.450218	61.33981	3.853112	0.0002815

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:factor(Time)8	121.59744	36.36762	48.07180	3.343564	0.0016092
factor(Time)1	37.88158	16.80689	49.14302	2.253931	0.0286938

CD27+IgD- switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)14	16.67333	5.808017	46.36242	2.870744	0.0061524
OutcomeDied:factor(Time)14	-23.76894	9.569855	46.52627	-2.483731	0.0166625
OutcomeDied:factor(Time)3	-20.00222	8.099103	47.20672	-2.469683	0.0171942
factor(Time)21	14.08234	6.267358	47.25499	2.246934	0.0293537
OutcomeDied:factor(Time)8	-16.05135	7.162484	46.77082	-2.241032	0.0298109
OutcomeDied:factor(Time)5	-15.19473	6.853678	46.39420	-2.217018	0.0315609
OutcomeAlive s/p OHT:factor(Time)14	-14.36929	6.997197	46.40850	-2.053579	0.0456758

MCP-1

	Estimate	Std. Error	df	t value	Pr(>|t|)

IFN-a2

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh:factor(Time)5	-83.38244	25.81665	48.86115	-3.229793	0.0022176
LowIntermacsHigh:factor(Time)8	-74.03854	26.08098	49.05338	-2.838794	0.0065708
LowIntermacsHigh:factor(Time)3	-71.00660	26.08098	49.05338	-2.722543	0.0089451
LowIntermacsHigh	64.16333	23.82257	40.13825	2.693384	0.0102726
factor(Time)8	53.76254	21.43059	49.46030	2.508682	0.0154438
factor(Time)5	42.82700	20.30165	48.76539	2.109532	0.0400558

CD19+CD5+CD11b+

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:factor(Time)21	18.60843	5.699112	56.68510	3.265145	0.0018584
VAD.IndicationDT:factor(Time)8	10.32734	4.156816	55.37079	2.484436	0.0160275

num lymph

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale:factor(Time)8	9.848232	2.746704	54.41204	3.585472	0.0007200
factor(Time)8	-8.159681	2.352215	54.37376	-3.468936	0.0010300
factor(Time)14	-8.101354	3.062814	55.12449	-2.645069	0.0106199
SexMale	-10.353852	3.922084	26.14988	-2.639885	0.0138005
factor(Time)5	-7.781354	3.062814	55.12449	-2.540590	0.0139123
SexMale:factor(Time)5	8.682914	3.494502	55.06965	2.484736	0.0160327
SexMale:factor(Time)3	5.898797	2.849382	54.68689	2.070202	0.0431688
SexMale:factor(Time)14	7.448953	3.625289	54.98976	2.054720	0.0446698

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:factor(Time)8	-27.59770	4.932075	46.11295	-5.595555	0.0000012
OutcomeDied post OHT	27.31810	7.270274	22.64763	3.757506	0.0010464
OutcomeDied post OHT:factor(Time)1	-13.37738	5.047487	45.94619	-2.650306	0.0109931

CD27-IgD+ mature naive

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)14	-19.34727	6.403688	54.77962	-3.021270	0.0038210
AgeGreater60older:factor(Time)14	17.50253	7.722534	54.66856	2.266424	0.0274049

CD19+CD27+

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:factor(Time)8	-30.68974	11.896519	46.35300	-2.579725	0.0131220
OutcomeDied:factor(Time)8	-18.18883	8.283555	46.91757	-2.195776	0.0330881
OutcomeDied:factor(Time)3	-19.62377	9.360583	47.47076	-2.096426	0.0413996

G-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+CD27-

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT:factor(Time)8	30.68584	11.990106	46.36468	2.559264	0.0138150
OutcomeDied:factor(Time)8	18.19237	8.347928	46.94556	2.179267	0.0343639
OutcomeDied:factor(Time)3	19.65350	9.432447	47.51498	2.083606	0.0425996

lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)

IL-5

	Estimate	Std. Error	df	t value	Pr(>|t|)
LowIntermacsHigh	14.33625	4.540629	50.69022	3.157327	0.0026812
LowIntermacsHigh:factor(Time)5	-16.35500	5.441291	49.36905	-3.005720	0.0041567
LowIntermacsHigh:factor(Time)3	-15.72446	5.494185	49.66232	-2.862019	0.0061466
LowIntermacsHigh:factor(Time)8	-13.08094	5.494185	49.66232	-2.380870	0.0211494
LowIntermacsHigh:factor(Time)1	-12.48229	5.441291	49.36905	-2.293994	0.0260840

MIP-1a

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:factor(Time)3	41.28308	12.390641	48.60325	3.331795	0.0016549
factor(Time)3	-11.46708	5.569941	49.22844	-2.058743	0.0448305

CD27-IgD- switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied:factor(Time)8	19.81291	9.256631	50.10634	2.140402	0.0372133

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied:factor(Time)5	7.770913	2.375060	48.89002	3.271880	0.0019634
OutcomeAlive s/p OHT:factor(Time)5	6.157210	2.440892	50.31147	2.522524	0.0148592
factor(Time)14	-4.926591	2.013314	49.03199	-2.447005	0.0180351
factor(Time)5	-3.476482	1.679134	48.86411	-2.070402	0.0437202

num lymph

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:factor(Time)21	218165.6	69573.59	5501.95	3.135753	0.0017232

IL-12(p40)

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older:factor(Time)3	-39.92823	16.10410	49.75241	-2.479383	0.0165940
AgeGreater60older:factor(Time)8	-39.09737	16.10410	49.75241	-2.427790	0.0188521
AgeGreater60older:factor(Time)5	-36.10684	15.62485	49.43068	-2.310860	0.0250524
factor(Time)8	29.12464	13.88649	50.03739	2.097336	0.0410371

CD27+IgD-IgM+ switched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)14	24.15202	6.766597	47.02757	3.569301	0.0008372
factor(Time)5	17.32212	5.643461	47.08561	3.069415	0.0035536
OutcomeAlive s/p OHT:factor(Time)14	-21.39064	8.148405	47.15503	-2.625132	0.0116397
OutcomeDied:factor(Time)14	-24.01191	11.131654	47.47898	-2.157084	0.0360919
OutcomeDied:factor(Time)5	-16.38868	7.982839	47.08336	-2.052989	0.0456533

CD27+IgD+IgM+ nonswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	-31.50752	9.007301	37.48747	-3.497997	0.0012251
SexMale:factor(Time)8	27.02760	8.735159	54.73131	3.094116	0.0031071
SexMale:factor(Time)3	24.44017	9.046112	55.34926	2.701732	0.0091385
factor(Time)8	-20.00903	7.482252	54.65772	-2.674199	0.0098594
factor(Time)3	-15.59653	7.482252	54.65772	-2.084470	0.0418040

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:factor(Time)8	-10.16212	3.121996	55.38520	-3.255008	0.0019363
RVADYes	11.57901	3.999725	34.08235	2.894952	0.0065730

CD27+IgD+IgM+ nonswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
SexMale	-31.50752	9.007301	37.48747	-3.497997	0.0012251
SexMale:factor(Time)8	27.02760	8.735159	54.73131	3.094116	0.0031071
SexMale:factor(Time)3	24.44017	9.046112	55.34926	2.701732	0.0091385
factor(Time)8	-20.00903	7.482252	54.65772	-2.674199	0.0098594
factor(Time)3	-15.59653	7.482252	54.65772	-2.084470	0.0418040

IFN-a2

	Estimate	Std. Error	df	t value	Pr(>|t|)
AgeGreater60older:factor(Time)5	-80.13405	27.57557	48.91764	-2.905980	0.0054855
AgeGreater60older	59.42952	25.37722	42.42564	2.341845	0.0239558

CD19+CD268+

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:factor(Time)3	-27.99543	10.480918	56.66595	-2.671086	0.0098514
RVADYes:factor(Time)8	-20.33650	9.017817	55.39965	-2.255146	0.0280950

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+CD268+

	Estimate	Std. Error	df	t value	Pr(>|t|)
Survivaldead:factor(Time)8	-21.73092	8.069861	55.34926	-2.692849	0.0093560
Survivaldead:factor(Time)3	-18.76413	8.701780	55.67109	-2.156355	0.0353913

CD19+CD5+

	Estimate	Std. Error	df	t value	Pr(>|t|)
VAD.IndicationDT:factor(Time)8	13.77699	5.156496	55.63322	2.671773	0.0098785
VAD.IndicationDT:factor(Time)21	18.57915	7.056032	57.34477	2.633087	0.0108560

IL-1b

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)8	7.942204	2.258115	47.94441	3.517183	0.0009652
LowIntermacsHigh:factor(Time)8	-8.641904	2.704957	47.76020	-3.194840	0.0024795
LowIntermacsHigh	5.410829	2.448628	37.85728	2.209739	0.0332453

G-CSF

	Estimate	Std. Error	df	t value	Pr(>|t|)

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD27+IgD+IgM+ nonswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
OutcomeDied post OHT	51.75397	18.81024	34.01848	2.751372	0.0094435
OutcomeDied post OHT:factor(Time)8	-45.26825	18.42339	46.22234	-2.457108	0.0178180

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)3	-60.31567	14.86319	41.40883	-4.058057	0.0002141
factor(Time)8	-56.86000	14.86319	41.40883	-3.825558	0.0004327
factor(Time)5	-56.01533	14.86319	41.40883	-3.768729	0.0005127
OutcomeAlive s/p OHT:factor(Time)3	63.98437	17.82884	41.70621	3.588813	0.0008664
OutcomeAlive s/p OHT:factor(Time)5	63.40940	18.10497	42.00334	3.502320	0.0011083
OutcomeAlive s/p OHT	-52.98502	15.21731	40.22085	-3.481892	0.0012142
OutcomeDied:factor(Time)8	67.94475	19.66215	41.40883	3.455611	0.0012815
OutcomeDied:factor(Time)3	67.52042	19.66215	41.40883	3.434030	0.0013633
OutcomeAlive s/p OHT:factor(Time)8	60.98559	17.82884	41.70621	3.420615	0.0014097
OutcomeDied	-55.94450	16.90936	39.24878	-3.308493	0.0020156
OutcomeDied:factor(Time)5	57.59233	19.66215	41.40883	2.929096	0.0055066
factor(Time)1	-48.29333	16.88835	42.69337	-2.859564	0.0065379
OutcomeAlive s/p OHT:factor(Time)1	48.18291	19.55600	42.67658	2.463842	0.0178540
OutcomeDied post OHT	-62.76000	25.56455	39.24878	-2.454962	0.0186237
OutcomeDied:factor(Time)1	48.27333	21.23446	42.22333	2.273348	0.0281535

IL-8

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+27-38+CD5+transitionals

	Estimate	Std. Error	df	t value	Pr(>|t|)

CD19+CD268+

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)14	-34.03871	8.813153	54.50836	-3.862263	0.0003005
AgeGreater60older:factor(Time)14	34.33335	10.627446	54.41277	3.230631	0.0020977
factor(Time)21	-17.19694	6.912037	54.73129	-2.487970	0.0159222
AgeGreater60older:factor(Time)3	19.32183	8.006711	54.60495	2.413204	0.0191972
factor(Time)8	-11.89000	5.511085	53.89410	-2.157470	0.0354451

CD27+IgD+ unswitched memory

	Estimate	Std. Error	df	t value	Pr(>|t|)
RVADYes:factor(Time)8	-10.16212	3.121996	55.38520	-3.255008	0.0019363
RVADYes	11.57901	3.999725	34.08235	2.894952	0.0065730

Eotaxin

	Estimate	Std. Error	df	t value	Pr(>|t|)

MIP-1a

	Estimate	Std. Error	df	t value	Pr(>|t|)
Survivaldead:factor(Time)3	32.83650	10.707160	48.95956	3.066780	0.0035187
factor(Time)3	-14.18310	6.214399	49.55959	-2.282297	0.0268003
Survivaldead:factor(Time)1	21.90146	10.827348	49.22900	2.022790	0.0485437

CD19+27+IgD-38++IgG ASC

	Estimate	Std. Error	df	t value	Pr(>|t|)

TNF-a

	Estimate	Std. Error	df	t value	Pr(>|t|)
factor(Time)3	-37.21699	12.78570	50.77795	-2.910831	0.0053410
factor(Time)5	-37.50816	13.62272	52.06253	-2.753353	0.0081034
factor(Time)8	-37.45827	13.62272	52.06253	-2.749690	0.0081827
SexMale:factor(Time)8	41.33081	15.82776	51.47185	2.611286	0.0117898
SexMale:factor(Time)3	39.58387	15.23657	50.60393	2.597952	0.0122521
factor(Time)1	-32.66139	12.78570	50.77795	-2.554526	0.0136757
SexMale:factor(Time)5	39.72646	15.94812	51.60286	2.490980	0.0159951
SexMale	-25.81619	12.80567	54.31830	-2.015997	0.0487575

CD19CD24hiCD38-memory

	Estimate	Std. Error	df	t value	Pr(>|t|)